Vattinul
Methods and systems of prioritizing treatments, vaccination, testing and/or activities while protecting the privacy of individuals
US11107588B2
United States
- Inventor
- Gal EHRLICH
- Maier Fenster
- Current Assignee
- Individual
2020-11-30
2021-03-18
2021-07-27
2021-08-31
2021-08-31
Application granted
Status
Active
2040-11-30
Anticipated expiration
Description
RELATED APPLICATIONS
139 candidate vaccines in preclinical evaluation
Weekly Mobility Data
This
application claims the benefit of priority of Israel Patent Application
No. 277083 filed on Sep. 1, 2020, Israel Patent Application No. 276665
filed on Aug. 11, 2020, and Israel Patent Application No. 276648 filed
on Aug. 11, 2020. The contents of the above applications are all
incorporated by reference as if fully set forth herein in their
entirety.
This
application is also related to United Arab Emirates Patent Application
No. P6001304/2020 filed on Sep. 17, 2020, the contents of which are
incorporated herein by reference in their entirety.
FIELD AND BACKGROUND OF THE INVENTION
The
present invention, in some embodiments thereof, relates to methods and
systems of prioritizing vaccinations\treatments\testing and, more
particularly, but not exclusively, to method and systems of prioritizing
vaccinations\treatments\testing in a pandemic situation, whereby
vaccines are at short supply and while protecting the privacy of the
individuals in the population.
Coronavirus
disease 2019 (COVID-19) is an infectious disease caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first
identified in December 2019 in Wuhan, Hubei, China, and has resulted in
an ongoing pandemic. The first confirmed case has been traced back to 17
Nov. 2019 in Hubei. As of 6 August 2020, more than 18.7 million cases
have been reported across 188 countries and territories, resulting in
more than 706,000 deaths. More than 11.3 million people have recovered.
The virus is primarily spread between people during close contact, most
often via small droplets produced by coughing, sneezing, and talking.
The droplets usually fall to the ground or onto surfaces rather than
travelling through air over long distances. However, the transmission
may also occur through smaller droplets that are able to stay suspended
in the air for longer periods of time in enclosed spaces, as typical for
airborne diseases. Less commonly, people may become infected by
touching a contaminated surface and then touching their face. It is most
contagious during the first three days after the onset of symptoms,
although spread is possible before symptoms appear, after they disappear
and from people who show very mild or do not show symptoms at all.
In
addition, about 5% of COVID-19 patients experience complications
including septic shock, acute respiratory distress syndrome (ARDS),
acute cardiac or kidney injury, and disseminated intravascular
coagulation (DIC). These complications are thought to be manifestations
of the cytokine storm triggered by the host immune response of the
virus. In critically ill patients, ARDS was the most common complication
in 67% of the patients with a 28-day mortality of 61.5%. DIC has been
widely reported in COVID-19. Pulmonary embolism (PE) in COVID-19
patients has been reported in a few studies. A recent study pointed to a
higher incidence of PE with 23% in severe COVID-19 patients. The
relationship between virally triggered inflammation, venous
thromboembolism, and ARDS in COVID-19 is still under investigation.
Given that patients with severe COVID-19 often present with shortness of
breath and pulmonary infiltrates, the diagnosis of PE may be overlooked
in the context of an ARDS diagnosis.
A research article by Straetemans et. al. called “Prioritization strategies for pandemic influenza vaccine in 27 countries of the European Union and the Global Health Security Action Group: a review”
discussed vaccine prioritization strategies during pandemic times, but
its conclusions are limited to the critical groups, for example, health
care providers (e.g., doctors, nurses, laboratories, hospitals, etc.),
essential service providers (e.g., police, fire fighters, public sector
personnel, governmental personnel, etc.) and high risk individuals
(e.g., people with high risk of complications, pregnant women, children,
etc.). These obvious groups usually amount to less than 2-10% of the
total population, which still leaves the government with the question of
what is the best order to vaccinate the rest of the population, namely
prioritizing vaccinations.
SUMMARY OF THE INVENTION
Following
is a non-exclusive list including some examples of embodiments of the
invention. The invention also includes embodiments, which include fewer
than all the features in an example, and embodiments using features from
multiple examples, also if not expressly listed below.
Example 1. An anonymized method of treating subjects against an infectious disease caused by a pathogen, comprising:
a. providing an electronic device with proximity tracking circuitry for each of said subjects;
b. generating an ID for each said electronic device;
c.
at a proximity event, when a particular said electronic device of a
particular said subject is in proximity of one or more other of said
electronic devices, one or both of transmitting said ID or an indication
thereof to said one or more other devices and receiving an ID or
indication thereof from said one or more other devices, by said
particular electronic device;
d.
generating, by said particular electronic device a score reflecting a
propensity for proximity, according to a plurality of received IDs;
e. generating for said particular electronic device a prioritization of treatment based on said score;
f. treating said particular subject according to said prioritization.
Example
2. The method according to example 1, wherein said generating an ID
comprises generating an ID having fewer than 100,000 potential values.
Example
3. The method according to example 2, wherein said generating an ID
comprises generating a unique ID and also generating said ID as a
portion of said unique ID.
Example 4. The method according to example 1, further comprising changing said ID periodically.
Example
5. The method according to example 1, further comprising generating a
second ID and transmitting said second ID or indication thereof together
with said ID.
Example
6. The method according to example 5, wherein said transmitting a
second ID is carried out only at a fraction of said proximity events.
Example
7. The method according to example 6, wherein said transmitting
comprises transmitting also second IDs previously received from others
of said electronic devices.
Example
8. The method according to example 6, comprising generating an
indication of closeness of a population met by said electronic device
based on said received second IDs.
Example
9. The method according to example 1, wherein said score depends on an
estimation of propensity of proximity of said one or more other devices.
Example 10. The method according to example 1, wherein said generating said score comprises counting the number of received IDs.
Example 11. The method according to example 10, wherein said counting comprises counting unique IDs.
Example
12. The method according to example 10, wherein said counting comprises
counting IDs with a weighted parameter, said weighted parameter is
generated by analyzing said exchanged second IDs.
Example
13. The method according to example 1, wherein said generating for said
particular device comprises transmitting said score to a server and
generating said prioritization on said server.
Example
14. The method according to example 13, wherein generating said
prioritization comprises comparing scores by different ones of said
electronic devices.
Example
15. The method according to example 1, wherein said generating for said
particular device comprises generating said prioritization on said
particular electronic device.
Example
16. The method according to example 15, wherein said generation
comprises receiving form a server a list or a function indication
prioritization according to score.
Example
17. The method according to example 1, comprising displaying treatment
instructions on said particular electronic device based on said
generated prioritization.
Example
18. The method of example 1, wherein said pathogen comprises a corona
virus and wherein said treatment comprises a vaccination and wherein
said prioritization is used to select subjects at greater risk of
transmitting the pathogen during a pandemic to be vaccinated sooner than
subjects less likely to transmit the pathogen.
Example
19. A system for anonymously selecting subjects for treatment against
an infectious disease caused by a pathogen, comprising:
a. a plurality of electronic devices configured to be carried around by said subjects and configured with instructions to:
-
- i. generate an ID comprising for each said electronic device;
- ii. when in proximity of another such electronic device, one or both of transmit said ID or an indication thereof to said another electronic device and receive an ID or indication thereof from said another electronic device;
- iii. generating, a score based on a plurality of such received IDs;
- iv. receiving information from a server;
- v. displaying relevant treatment instructions to said subjects based on said received information;
b. at least one server comprising a memory and a plurality of modules; said memory-comprising instructions for:
-
- vi.
sending to said plurality of electronic devices information usable by a
circuitry in said plurality of electronic devices to display said
relevant treatment instructions,
wherein said at least one server or said electronic devices comprise instructions to generate a prediction of likelihood of a subject transmitting said pathogen, based on a score of the subject.
- vi.
sending to said plurality of electronic devices information usable by a
circuitry in said plurality of electronic devices to display said
relevant treatment instructions,
Example 20. The system according to example 19, wherein said information comprises one or more of subject specific information.
Example
21. The system according to example 19, wherein said information
comprises general information usable by a plurality of subjects and
devices thereof.
Example
22. The system according to example 19, wherein said server is
configured with instructions to receive anonymous scores for a plurality
of said electronic devices and use said received scores to generate
said general information, said electronic devices configured to use said
general information to determine a relative treatment priority for
their respective subjects.
Example
23. The system according to example 19, wherein said electronic devices
comprises a proximity-detecting module using one or more of:
a. physical proximity data received by means of electronic positioning data of said subject;
b.
a distance indicating sensor which indicates physical proximity of the
location of a device in relation to the location of said another device;
and
c. historical location data.
Example
24. The system according to example 19, wherein said at least one
server or said electronic devices comprise instructions to determine a
treatment prioritization based on said likelihood.
Example
25. The system according to example 23, wherein said determine a
treatment prioritization further comprises one or more of:
a. generating a score component based on a nature of a location where said physical proximity data is related;
b. generating a score component comprising health data of the subject of one or both electronic devices;
c. generating a score component comprising a profession of the subject of one or both electronic devices;
d. generating a score component reflecting relative health risk to said subject if said subject contracts said pathogen; and
e. generating a score component reflecting damage to society if said subject contracts said pathogen.
Example
26. The system according to example 23, wherein when said physical
proximity data is related to a location that is either indoors or in a
closed space, then said predicted likelihood of said subject of
transmitting said pathogen increases by a factor of between about 10
times to about 100 times.
Example
27. The system according to example 19, further comprising a
vaccination server, which allocates vaccinations for a corona virus
according to, said displayed treatment information.
Example
28. The system according to example 27, wherein said server comprises a
simulation module configured to perform one or both of:
(a) predict the effect of vaccination on disease spread;
(b)
predict the effect of an ID transmission probability on distinguishing
between subjects who contact mainly subjects in a same subpopulation.
Example
29. The system of example 19, wherein said electronic devices are
configured to transmit a second ID and previously received second IDs,
at a probability of less than 10% and using said received second IDs to
generate said score.
Example
30. The system of example 19, wherein said transmitted ID is a
non-unique ID having fewer possible values than 10% of the number of
said devices.
According
to an aspect of some embodiments of the present invention there is
provided a method of selecting subjects for being vaccinated/treated
against an infectious disease caused by a pathogen, using personal
physical proximity information of a subject, comprising:
a.
generating, by circuitry, a predicted likelihood of said subject of
transmitting said pathogen based on said physical proximity information,
for a plurality of subjects;
b.
selecting subjects of said plurality of subjects for
vaccinating/treating based on a prediction that said
vaccinating/treating said subjects will reduce a likelihood of spreading
of said disease in said plurality of subjects, wherein said selecting
is based on said generated predicted likelihood.
According
to some embodiments of the invention, said pathogen is selected from
the group consisting of a virus, a bacterium, a fungus and a protozoan.
According to some embodiments of the invention, said disease is endemic or pandemic.
According
to some embodiments of the invention, said predicted likelihood of said
subject of transmitting said pathogen comprises one or more score
components used for generating a score.
According
to some embodiments of the invention, said score relates to a predicted
likelihood of a group of subjects transmitting said pathogen based on
said physical proximity information, and said physical proximity
information is a first score component used for said generating said
score.
According
to some embodiments of the invention, said generating said score
further comprises a score component based on a nature of a location
where said physical proximity information is related.
According
to some embodiments of the invention, said nature of the location is
one or more of an open space, a closed space, indoor, outdoor,
ventilated indoor space, non-ventilated indoor space and any combination
thereof.
According
to some embodiments of the invention, when said physical proximity
information is related to a location that is either indoors or in a
closed space, then said predicted likelihood of said subject of
transmitting said pathogen increases by a factor of between about 10
times to about 100 times.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data received by means of electronic
positioning data of said subject.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data of the location of said subject
in relation to the location of other subjects.
According
to some embodiments of the invention, said physical proximity data
comprises one or more of physical proximity distance data, duration of
physical proximity data and/or ambience of physical proximity data.
According
to some embodiments of the invention, said electronic positioning data
is one or more of electronic geographical positioning data of said
subject, electronic proximity positioning data of said subject relative
to other subjects.
According
to some embodiments of the invention, said method further comprises
generating a predicted likelihood of said subject contracting said
pathogen based on said physical proximity data.
According
to some embodiments of the invention, said generating a score further
comprises a second score component based on said predicted likelihood of
said subject contracting said pathogen based on said physical proximity
data.
According
to some embodiments of the invention, said electronic positioning data
is collected using one or more electronic devices.
According
to some embodiments of the invention, said one or more electronic
devices are one or more of a smartphone, a tablet, a smartwatch and a
dedicated electronic device.
According
to some embodiments of the invention, the method further comprising
vaccinating/treating said subjects according to said score.
According
to some embodiments of the invention, said generating a score further
comprises a third score component reflecting relative health risk to
said subject if said subject contracts said pathogen.
According
to some embodiments of the invention, said generating a score further
comprises a fourth score component reflecting damage to society if said
subject contracts said pathogen.
According to some embodiments of the invention, said electronic positioning data comprises geographical location data.
According to some embodiments of the invention, said physical proximity information comprises historical location data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising historical health data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising a profession in record of said
subject.
According
to some embodiments of the invention, said physical proximity
information further comprises information received from a third party.
According to some embodiments of the invention, said physical proximity information is provided by said subject actively.
According
to some embodiments of the invention, said physical proximity
information is provided by said subject passively by means of said one
or more electronic devices.
According to some embodiments of the invention, said pathogen is a virus.
According to some embodiments of the invention, said virus is a corona virus.
According to some embodiments of the invention, said virus is SARS-CoV.
According to some embodiments of the invention, said virus is MERS-CoV.
According to some embodiments of the invention, said virus is SARS-CoV-2.
According to some embodiments of the invention, said virus is an influenza virus.
According to some embodiments of the invention, said disease results in influenza like symptoms.
According
to an aspect of some embodiments of the present invention there is
provided a method of selecting subjects for being vaccinated/treated
against an infectious disease caused by a pathogen, comprising:
a. automatically collecting physical proximity information of a subject with other subjects;
b. generating a predicted likelihood of said subject of transmitting said virus based on said physical proximity information;
c.
generating a score comprising a first score component based on said
predicted likelihood of said subject of transmitting said virus;
d. repeating steps b-c for a plurality of subjects; and
e. prioritizing vaccination/treatment of said subjects according to said score.
According
to some embodiments of the invention, said pathogen is selected from
the group consisting of a virus, a bacterium, a fungus and a protozoan.
According to some embodiments of the invention, said disease is endemic or pandemic.
According
to some embodiments of the invention, said generating said score
further comprises a score component based on a nature of a location
where said physical proximity information is related.
According
to some embodiments of the invention, said nature of the location is
one or more of an open space, a closed space, indoor, outdoor,
ventilated indoor space, non-ventilated indoor space and any combination
thereof.
According
to some embodiments of the invention, when said physical proximity
information is related to a location that is either indoors or in a
closed space, then said predicted likelihood of said subject of
transmitting said pathogen increases by a factor of between about 10
times to about 100 times.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data received by means of electronic
positioning data of said subject.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data of the location of said subject
in relation to the location of other subjects.
According
to some embodiments of the invention, said physical proximity data
comprises one or more of physical proximity distance data, duration of
physical proximity data and/or ambience of physical proximity data.
According
to some embodiments of the invention, said electronic positioning data
is one or more of electronic geographical positioning data of said
subject, electronic proximity positioning data of said subject relative
to other subjects.
According
to some embodiments of the invention, said method further comprises
generating a predicted likelihood of said subject contracting said
pathogen based on said physical proximity data.
According
to some embodiments of the invention, said generating a score further
comprises a second score component based on said predicted likelihood of
said subject contracting said pathogen based on said physical proximity
data.
According
to some embodiments of the invention, said electronic positioning data
is collected using one or more electronic devices.
According
to some embodiments of the invention, said one or more electronic
devices are one or more of a smartphone, a tablet, a smartwatch and a
dedicated electronic device.
According
to some embodiments of the invention, the method further comprising
vaccinating/treating said subjects according to said score.
According
to some embodiments of the invention, said generating a score further
comprises a third score component reflecting relative health risk to
said subject if said subject contracts said pathogen.
According
to some embodiments of the invention, said generating a score further
comprises a fourth score component reflecting damage to society if said
subject contracts said pathogen.
According to some embodiments of the invention, said electronic positioning data comprises geographical location data.
According to some embodiments of the invention, said physical proximity information comprises historical location data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising historical health data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising a profession in record of said
subject.
According
to some embodiments of the invention, said physical proximity
information further comprises information received from a third party.
According to some embodiments of the invention, said physical proximity information is provided by said subject actively.
According
to some embodiments of the invention, said physical proximity
information is provided by said subject passively by means of said one
or more electronic devices.
According to some embodiments of the invention, said pathogen is a virus.
According to some embodiments of the invention, said virus is a corona virus.
According to some embodiments of the invention, said virus is SARS-CoV.
According to some embodiments of the invention, said virus is MERS-CoV.
According to some embodiments of the invention, said virus is SARS-CoV-2.
According to some embodiments of the invention, said virus is an influenza virus.
According to some embodiments of the invention, said disease results in influenza like symptoms.
According
to an aspect of some embodiments of the present invention there is
provided a system for selecting subjects for being vaccinated/treated
against an infectious disease caused by a pathogen, comprising:
a. at least one server comprising a memory;
b. an analytics module;
c. a database module;
d. a simulation module;
said memory in said at least one server comprising instructions, said instructions comprising:
-
- i. generating, by circuitry, a predicted likelihood of said subject of transmitting said pathogen based on said physical proximity information, for a plurality of subjects;
- ii. selecting subjects of said plurality of subjects for vaccinating/treating based on a prediction that said vaccinating/treating said subjects will reduce a likelihood of spreading of said disease in said plurality of subjects, wherein said selecting is based on said generated predicted likelihood.
According
to some embodiments of the invention, said pathogen is selected from
the group consisting of a virus, a bacterium, a fungus and a protozoan.
According to some embodiments of the invention, said disease is endemic or pandemic.
According
to some embodiments of the invention, said predicted likelihood of said
subject of transmitting said pathogen comprises one or more score
components used for generating a score.
According
to some embodiments of the invention, said score relates to a predicted
likelihood of a group of subjects transmitting said pathogen based on
said physical proximity information, and said physical proximity
information is a first score component used for said generating said
score.
According
to some embodiments of the invention, said generating said score
further comprises a score component based on a nature of a location
where said physical proximity information is related.
According
to some embodiments of the invention, said nature of the location is
one or more of an open space, a closed space, indoor, outdoor,
ventilated indoor space, non-ventilated indoor space and any combination
thereof.
According
to some embodiments of the invention, when said physical proximity
information is related to a location that is either indoors or in a
closed space, then said predicted likelihood of said subject of
transmitting said pathogen increases by a factor of between about 10
times to about 100 times.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data received by means of electronic
positioning data of said subject.
According
to some embodiments of the invention, said physical proximity
information is physical proximity data of the location of said subject
in relation to the location of other subjects.
According
to some embodiments of the invention, said physical proximity data
comprises one or more of physical proximity distance data, duration of
physical proximity data and/or ambience of physical proximity data.
According
to some embodiments of the invention, said electronic positioning data
is one or more of electronic geographical positioning data of said
subject, electronic proximity positioning data of said subject relative
to other subjects.
According
to some embodiments of the invention, said method further comprises
generating a predicted likelihood of said subject contracting said
pathogen based on said physical proximity data.
According
to some embodiments of the invention, said generating a score further
comprises a second score component based on said predicted likelihood of
said subject contracting said pathogen based on said physical proximity
data.
According
to some embodiments of the invention, said electronic positioning data
is collected using one or more electronic devices.
According
to some embodiments of the invention, said one or more electronic
devices are one or more of a smartphone, a tablet, a smartwatch and a
dedicated electronic device.
According
to some embodiments of the invention, the system further comprising
vaccinating/treating said subjects according to said score.
According
to some embodiments of the invention, said generating a score further
comprises a third score component reflecting relative health risk to
said subject if said subject contracts said pathogen.
According
to some embodiments of the invention, said generating a score further
comprises a fourth score component reflecting damage to society if said
subject contracts said pathogen.
According to some embodiments of the invention, said electronic positioning data comprises geographical location data.
According to some embodiments of the invention, said physical proximity information comprises historical location data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising historical health data.
According
to some embodiments of the invention, said generating said score
further comprises a component comprising a profession in record of said
subject.
According
to some embodiments of the invention, said physical proximity
information further comprises information received from a third party.
According to some embodiments of the invention, said physical proximity information is provided by said subject actively.
According
to some embodiments of the invention, said physical proximity
information is provided by said subject passively by means of said one
or more electronic devices.
According to some embodiments of the invention, said simulation module further comprises a prediction module.
According to some embodiments of the invention, said pathogen is a virus.
According to some embodiments of the invention, said virus is a corona virus.
According to some embodiments of the invention, said virus is SARS-CoV.
According to some embodiments of the invention, said virus is MERS-CoV.
According to some embodiments of the invention, said virus is SARS-CoV-2.
According to some embodiments of the invention, said virus is an influenza virus.
According to some embodiments of the invention, said disease results in influenza like symptoms.
Following
is a second non-exclusive list including some examples of embodiments
of the invention. The invention also includes embodiments, which include
fewer than all the features in an example, and embodiments using
features from multiple examples, also if not expressly listed below.
Example
1. A method of selecting subjects for being vaccinated against an
infectious disease caused by a pathogen, using personal physical
proximity information of a subject, comprising:
a.
generating, by circuitry, a predicted likelihood of said subject of
transmitting said pathogen based on said physical proximity information,
for a plurality of subjects;
b.
selecting subjects of said plurality of subjects for vaccinating based
on a prediction that said vaccinating said subjects will reduce a
likelihood of spreading of said disease in said plurality of subjects,
wherein said selecting is based on said generated predicted likelihood.
Example
2. The method according to example 1, wherein said pathogen is selected
from the group consisting of a virus, a bacterium, a fungus and a
protozoan.
Example 3. The method according to according to any one of examples 1-2, wherein said disease is endemic or pandemic.
Example
4. The method according to any one of examples 1-3, wherein said
predicted likelihood of said subject of transmitting said pathogen
comprises one or more score components used for generating a score.
Example
5. The method according to example 4, wherein said score relates to a
predicted likelihood of a group of subjects transmitting said pathogen
based on said physical proximity information, and said physical
proximity information is a first score component used for said
generating said score.
Example
6. The method according to any one of examples 4-5, wherein said
generating said score further comprises a score component based on a
nature of a location where said physical proximity information is
related.
Example
7. The method of example 6, wherein said nature of the location is one
or more of an open space, a closed space, indoor, outdoor, ventilated
indoor space, non-ventilated indoor space and any combination thereof.
Example
8. The method according to any one of examples 1-7, wherein when said
physical proximity information is related to a location that is either
indoors or in a closed space, then said predicted likelihood of said
subject of transmitting said pathogen increases by a factor of between
about 10 times to about 100 times.
Example
9. The method according to any one of examples 1-8, wherein said
physical proximity information is physical proximity data received by
means of electronic positioning data of said subject.
Example
10. The method according to any one of examples 1-9, wherein said
physical proximity information is physical proximity data of the
location of said subject in relation to the location of other subjects.
Example
11. The method according to any one of examples 9-10, wherein said
physical proximity data comprises one or more of physical proximity
distance data, duration of physical proximity data and/or ambience of
physical proximity data.
Example
12. The method according to any one of examples 9-11, wherein said
electronic positioning data is one or more of electronic geographical
positioning data of said subject, electronic proximity positioning data
of said subject relative to other subjects.
Example
13. The method according to any one of examples 1-12, wherein said
method further comprises generating a predicted likelihood of said
subject contracting said pathogen based on said physical proximity data.
Example
14. The method according to any one of examples 4-13, wherein said
generating a score further comprises a second score component based on
said predicted likelihood of said subject contracting said pathogen
based on said physical proximity data.
Example
15. The method according to any one of examples 9-14, wherein said
electronic positioning data is collected using one or more electronic
devices.
Example
16. The method of example 15, wherein said one or more electronic
devices are one or more of a smartphone, a tablet, a smartwatch and a
dedicated electronic device.
Example
17. The method according to any one of examples 4-16, further
comprising vaccinating said subjects according to said score.
Example
18. The method according to any one of examples 4-17, wherein said
generating a score further comprises a third score component reflecting
relative health risk to said subject if said subject contracts said
pathogen.
Example
19. The method according to any one of examples 4-18, wherein said
generating a score further comprises a fourth score component reflecting
damage to society if said subject contracts said pathogen.
Example
20. The method according to any one of examples 9-19, wherein said
electronic positioning data comprises geographical location data.
Example
21. The method according to any one of examples 1-20, wherein said
physical proximity information comprises historical location data.
Example
22. The method according to any one of examples 4-21, wherein said
generating said score further comprises a component comprising
historical health data.
Example
23. The method according to any one of examples 4-22, wherein said
generating said score further comprises a component comprising a
profession in record of said subject.
Example
24. The method according to any one of examples 1-23, wherein said
physical proximity information further comprises information received
from a third party.
Example
25. The method according to any one of examples 1-24, wherein said
physical proximity information is provided by said subject actively.
Example
26. The method according to any one of examples 1-25, wherein said
physical proximity information is provided by said subject passively by
means of said one or more electronic devices.
Example 27. The method according to any one of examples 1-26, wherein said pathogen is a virus.
Example 28. The method according to any one of examples 1-27, wherein said virus is a corona virus.
Example 29. The method according to any one of examples 1-28, wherein said virus is SARS-CoV.
Example 30. The method according to any one of examples 1-28, wherein said virus is MERS-CoV.
Example 31. The method according to any one of examples 1-28, wherein said virus is SARS-CoV-2.
Example 32. The method according to any one of examples 1-27, wherein said virus is an influenza virus.
Example 33. The method according to any one of examples 1-32, wherein said disease results in influenza like symptoms.
Example 34. A method of selecting subjects for being vaccinated against an infectious disease caused by a pathogen, comprising:
a. automatically collecting physical proximity information of a subject with other subjects;
b. generating a predicted likelihood of said subject of transmitting said virus based on said physical proximity information;
c.
generating a score comprising a first score component based on said
predicted likelihood of said subject of transmitting said virus;
d. repeating steps b-c for a plurality of subjects; and
e. prioritizing vaccination of said subjects according to said score.
Example
35. The method according to example 34, wherein said pathogen is
selected from the group consisting of a virus, a bacterium, a fungus and
a protozoan.
Example 36. The method according to any one of examples 34-35, wherein said disease is endemic or pandemic.
Example
37. The method according to any one of examples 34-36, wherein said
generating said score further comprises a score component based on a
nature of a location where said physical proximity information is
related.
Example
38. The method according to any one of examples 34-37, wherein said
nature of the location is one or more of an open space, a closed space,
indoor, outdoor, ventilated indoor space, non-ventilated indoor space
and any combination thereof.
Example
39. The method according to any one of examples 34-38, wherein when
said physical proximity information is related to a location that is
either indoors or in a closed space, then said predicted likelihood of
said subject of transmitting said pathogen increases by a factor of
between about 10 times to about 100 times. Example 40. The method
according to any one of examples 34-39, wherein said physical proximity
information is physical proximity data received by means of electronic
positioning data of said subject.
Example
41. The method according to any one of examples 34-40, wherein said
physical proximity information is physical proximity data of the
location of said subject in relation to the location of other subjects.
Example
42. The method according to any one of examples 38-41, wherein said
physical proximity data comprises one or more of physical proximity
distance data, duration of physical proximity data and/or ambience of
physical proximity data.
Example
43. The method according to any one of examples 38-42, wherein said
electronic positioning data is one or more of electronic geographical
positioning data of said subject, electronic proximity positioning data
of said subject relative to other subjects.
Example
44. The method according to any one of examples 38-43, wherein said
method further comprises generating a predicted likelihood of said
subject contracting said pathogen based on said physical proximity data.
Example
45. The method according to any one of examples 34-44, wherein said
generating a score further comprises a second score component based on
said predicted likelihood of said subject contracting said pathogen
based on said physical proximity data.
Example
46. The method according to any one of examples 38-45, wherein said
electronic positioning data is collected using one or more electronic
devices. Example 47. The method according to example 46, wherein said
one or more electronic devices are one or more of a smartphone, a
tablet, a smartwatch and a dedicated electronic device.
Example
48. The method according to any one of examples 34-47, further
comprising vaccinating said subjects according to said score.
Example
49. The method according to any one of examples 34-48, wherein said
generating a score further comprises a third score component reflecting
relative health risk to said subject if said subject contracts said
pathogen.
Example
50. The method according to any one of examples 34-49, wherein said
generating a score further comprises a fourth score component reflecting
damage to society if said subject contracts said pathogen.
Example
51. The method according to any one of examples 38-50, wherein said
electronic positioning data comprises geographical location data.
Example
52. The method according to any one of examples 34-51, wherein said
physical proximity information comprises historical location data.
Example
53. The method according to any one of examples 34-52, wherein said
generating said score further comprises a component comprising
historical health data.
Example
54. The method according to any one of examples 34-53, wherein said
generating said score further comprises a component comprising a
profession in record of said subject.
Example
55. The method according to any one of examples 34-54, wherein said
physical proximity information further comprises information received
from a third party.
Example
56. The method according to any one of examples 34-55, wherein said
physical proximity information is provided by said subject actively.
Example
57. The method according to any one of examples 34-56, wherein said
physical proximity information is provided by said subject passively by
means of said one or more electronic devices.
Example 58. The method according to any one of examples 34-57, wherein said pathogen is a virus.
Example 59. The method according to any one of examples 34-58, wherein said virus is a corona virus.
Example 60. The method according to any one of examples 34-58, wherein said virus is SARS-CoV.
Example 61. The method according to any one of examples 34-58, wherein said virus is MERS-CoV.
Example 62. The method according to any one of examples 34-58, wherein said virus is SARS-CoV-2.
Example 63. The method according to any one of examples 1-57, wherein said virus is an influenza virus.
Example 64. The method according to any one of examples 1-63, wherein said disease results in influenza like symptoms.
Example 65. A system for selecting subjects for being vaccinated against an infectious disease caused by a pathogen, comprising:
a. at least one server comprising a memory;
b. an analytics module;
c. a database module;
d. a simulation module;
said memory in said at least one server comprising instructions, said instructions comprising:
i.
generating, by circuitry, a predicted likelihood of said subject of
transmitting said pathogen based on said physical proximity information,
for a plurality of subjects;
ii.
selecting subjects of said plurality of subjects for vaccinating based
on a prediction that said vaccinating said subjects will reduce a
likelihood of spreading of said disease in said plurality of subjects,
wherein said selecting is based on said generated predicted likelihood.
Example
66. The system according to example 65, wherein said pathogen is
selected from the group consisting of a virus, a bacterium, a fungus and
a protozoan.
Example 67. The system according to any one of examples 65-66, wherein said disease is endemic or pandemic.
Example
68. The system according to any one of examples 65-67, wherein said
predicted likelihood of said subject of transmitting said pathogen
comprises one or more score components used for generating a score.
Example
69. The system according to example 68, wherein said score relates to a
predicted likelihood of a group of subjects transmitting said pathogen
based on said physical proximity information, and said physical
proximity information is a first score component used for said
generating said score.
Example
70. The system according to any one of examples 64-69, wherein said
generating said score further comprises a score component based on a
nature of a location where said physical proximity information is
related.
Example
71. The system of example 70, wherein said nature of the location is
one or more of an open space, a closed space, indoor, outdoor,
ventilated indoor space, non-ventilated indoor space and any combination
thereof.
Example
72. The system according to any one of examples 65-71, wherein when
said physical proximity information is related to a location that is
either indoors or in a closed space, then said predicted likelihood of
said subject of transmitting said pathogen increases by a factor of
between about 10 times to about 100 times.
Example
73. The system according to any one of examples 65-72, wherein said
physical proximity information is physical proximity data received by
means of electronic positioning data of said subject.
Example
74. The system according to any one of examples 65-73, wherein said
physical proximity information is physical proximity data of the
location of said subject in relation to the location of other subjects.
Example
75. The system according to any one of examples 69-74, wherein said
physical proximity data comprises one or more of physical proximity
distance data, duration of physical proximity data and/or ambience of
physical proximity data.
Example
76. The system according to any one of examples 69-75, wherein said
electronic positioning data is one or more of electronic geographical
positioning data of said subject, electronic proximity positioning data
of said subject relative to other subjects.
Example
77. The system according to any one of examples 65-76, wherein said
method further comprises generating a predicted likelihood of said
subject contracting said pathogen based on said physical proximity data.
Example
78. The system according to any one of examples 64-77, wherein said
generating a score further comprises a second score component based on
said predicted likelihood of said subject contracting said pathogen
based on said physical proximity data.
Example
79. The system according to any one of examples 69-78, wherein said
electronic positioning data is collected using one or more electronic
devices.
Example
80. The system according to example 79, wherein said one or more
electronic devices are one or more of a smartphone, a tablet, a
smartwatch and a dedicated electronic device.
Example
81. The system according to any one of examples 64-80, further
comprising vaccinating said subjects according to said score.
Example
82. The system according to any one of examples 64-81, wherein said
generating a score further comprises a third score component reflecting
relative health risk to said subject if said subject contracts said
pathogen.
Example
83. The system according to any one of examples 64-82, wherein said
generating a score further comprises a fourth score component reflecting
damage to society if said subject contracts said pathogen.
Example
84. The system according to any one of examples 69-83, wherein said
electronic positioning data comprises geographical location data.
Example
85. The system according to any one of examples 65-84, wherein said
physical proximity information comprises historical location data.
Example
86. The system according to any one of examples 64-85, wherein said
generating said score further comprises a component comprising
historical health data.
Example
87. The system according to any one of examples 64-86, wherein said
generating said score further comprises a component comprising a
profession in record of said subject.
Example
88. The system according to any one of examples 65-87, wherein said
physical proximity information further comprises information received
from a third party.
Example
89. The system according to any one of examples 65-88, wherein said
physical proximity information is provided by said subject actively.
Example
90. The system according to any one of examples 65-89, wherein said
physical proximity information is provided by said subject passively by
means of said one or more electronic devices.
Example
91. The system according to any one of examples 65-90, wherein said
simulation module further comprises a prediction module.
Example 92. The system according to any one of examples 65-91, wherein said pathogen is a virus.
Example 93. The system according to any one of examples 65-92, wherein said virus is a corona virus.
Example 94. The system according to any one of examples 65-92, wherein said virus is SARS-CoV.
Example 95. The system according to any one of examples 65-92, wherein said virus is MERS-CoV.
Example 96. The system according to any one of examples 65-91, wherein said virus is SARS-CoV-2.
Example 97. The system according to any one of examples 65-91, wherein said virus is an influenza virus.
Example 98. The system according to any one of examples 65-92 wherein said disease results in influenza like symptoms.
Unless
otherwise defined, all technical and/or scientific terms used herein
have the same meaning as commonly understood by one of ordinary skill in
the art to which the invention pertains. Although methods and materials
similar or equivalent to those described herein can be used in the
practice or testing of embodiments of the invention, exemplary methods
and/or materials are described below. In case of conflict, the patent
specification, including definitions, will control. In addition, the
materials, methods, and examples are illustrative only and are not
intended to be necessarily limiting.
As
will be appreciated by one skilled in the art, some embodiments of the
present invention may be embodied as a system, method or computer
program product. Accordingly, some embodiments of the present invention
may take the form of an entirely hardware embodiment, an entirely
software embodiment (including firmware, resident software, micro-code,
etc.) or an embodiment combining software and hardware aspects that may
all generally be referred to herein as a “circuit,” “module” or
“system.” Furthermore, some embodiments of the present invention may
take the form of a computer program product embodied in one or more
computer readable medium(s) having computer readable program code
embodied thereon. Implementation of the method and/or system of some
embodiments of the invention can involve performing and/or completing
selected tasks manually, automatically, or a combination thereof.
Moreover, according to actual instrumentation and equipment of some
embodiments of the method and/or system of the invention, several
selected tasks could be implemented by hardware, by software or by
firmware and/or by a combination thereof, e.g., using an operating
system.
For
example, hardware for performing selected tasks according to some
embodiments of the invention could be implemented as a chip or a
circuit. As software, selected tasks according to some embodiments of
the invention could be implemented as a plurality of software
instructions being executed by a computer using any suitable operating
system. In an exemplary embodiment of the invention, one or more tasks
according to some exemplary embodiments of method and/or system as
described herein are performed by a data processor, such as a computing
platform for executing a plurality of instructions. Optionally, the data
processor includes a volatile memory for storing instructions and/or
data and/or a non-volatile storage, for example, a magnetic hard-disk
and/or removable media, for storing instructions and/or data.
Optionally, a network connection is provided as well. A display and/or a
user input device such as a keyboard or mouse are optionally provided
as well.
Any
combination of one or more computer readable medium(s) may be utilized
for some embodiments of the invention. The computer readable medium may
be a computer readable signal medium or a computer readable storage
medium. A computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic, infrared,
or semiconductor system, apparatus, or device, or any suitable
combination of the foregoing. More specific examples (a non-exhaustive
list) of the computer readable storage medium would include the
following: an electrical connection having one or more wires, a portable
computer diskette, a hard disk, a random access memory (RAM), a
read-only memory (ROM), an erasable programmable read-only memory (EPROM
or Flash memory), an optical fiber, a portable compact disc read-only
memory (CD-ROM), an optical storage device, a magnetic storage device,
or any suitable combination of the foregoing. In the context of this
document, a computer readable storage medium may be any tangible medium
that can contain, or store a program for use by or in connection with an
instruction execution system, apparatus, or device.
A
computer readable signal medium may include a propagated data signal
with computer readable program code embodied therein, for example, in
baseband or as part of a carrier wave. Such a propagated signal may take
any of a variety of forms, including, but not limited to,
electro-magnetic, optical, or any suitable combination thereof. A
computer readable signal medium may be any computer readable medium that
is not a computer readable storage medium and that can communicate,
propagate, or transport a program for use by or in connection with an
instruction execution system, apparatus, or device.
Program
code embodied on a computer readable medium and/or data used thereby
may be transmitted using any appropriate medium, including but not
limited to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
Computer
program code for carrying out operations for some embodiments of the
present invention may be written in any combination of one or more
programming languages, including an object oriented programming language
such as Java, Smalltalk, C++ or the like and conventional procedural
programming languages, such as the “C” programming language or similar
programming languages. The program code may execute entirely on the
user's computer, partly on the user's computer, as a stand-alone
software package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the latter
scenario, the remote computer may be connected to the user's computer
through any type of network, including a local area network (LAN) or a
wide area network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet Service
Provider).
Some
embodiments of the present invention may be described below with
reference to flowchart illustrations and/or block diagrams of methods,
apparatus (systems) and computer program products according to
embodiments of the invention. It will be understood that each block of
the flowchart illustrations and/or block diagrams, and combinations of
blocks in the flowchart illustrations and/or block diagrams, can be
implemented by computer program instructions. These computer program
instructions may be provided to a processor of a general purpose
computer, special purpose computer, or other programmable data
processing apparatus to produce a machine, such that the instructions,
which execute via the processor of the computer or other programmable
data processing apparatus, create means for implementing the
functions/acts specified in the flowchart and/or block diagram block or
blocks.
These
computer program instructions may also be stored in a computer readable
medium that can direct a computer, other programmable data processing
apparatus, or other devices to function in a particular manner, such
that the instructions stored in the computer readable medium produce an
article of manufacture including instructions which implement the
function/act specified in the flowchart and/or block diagram block or
blocks.
The
computer program instructions may also be loaded onto a computer, other
programmable data processing apparatus, or other devices to cause a
series of operational steps to be performed on the computer, other
programmable apparatus or other devices to produce a computer
implemented process such that the instructions which execute on the
computer or other programmable apparatus provide processes for
implementing the functions/acts specified in the flowchart and/or block
diagram block or blocks.
Some
of the methods described herein are generally designed only for use by a
computer, and may not be feasible or practical for performing purely
manually, by a human expert. A human expert who wanted to manually
perform similar tasks might be expected to use completely different
methods, e.g., making use of expert knowledge and/or the pattern
recognition capabilities of the human brain, which would be vastly more
efficient than manually going through the steps of the methods described
herein.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
Some
embodiments of the invention are herein described, by way of example
only, with reference to the accompanying drawings. With specific
reference now to the drawings in detail, it is stressed that the
particulars shown are by way of example and for purposes of illustrative
discussion of embodiments of the invention. In this regard, the
description taken with the drawings makes apparent to those skilled in
the art how embodiments of the invention may be practiced.
In the drawings:
FIG. 1 is a schematic illustration of an exemplary definition of a superspreader, according to some embodiments of the invention;
FIG. 2 is a flowchart of an exemplary embodiment of the invention, according to some embodiments of the invention;
FIG. 3 is a schematic flowchart of a method of calculating a weighted score, according to some embodiments of the invention;
FIG. 4 is a schematic representation of an exemplary spreading network, according to some embodiments of the invention;
FIGS. 5a-f are
flowcharts of exemplary methods for identifying superspreaders with
high levels of anonymization, according to some embodiments of the
invention;
FIG. 6 is a flowchart of a method of generating a score, according to some embodiments of the invention; and
FIG. 7 is a schematic representation of an exemplary system, according to some embodiments of the invention.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
The
present invention, in some embodiments thereof, relates to methods and
systems of prioritizing vaccination/treatment and, more particularly,
but not exclusively, to methods and systems of prioritizing
vaccination/treatment in a pandemic situation.
Overview
A
broad aspect of some embodiments of the invention relates to reduce a
pandemic by reducing a k value of infection in addition to and/or at the
expense of reducing an R0 value thereof. In some embodiments of the
invention, this is achieved by identifying and vaccinating (or otherwise
preventing infection by) persons who are potential super spreaders
(e.g., people who, on the average, are expected to infect more than the
average, for example, 1, 2, 3 or more or intermediate values of standard
deviations from such average. This may result in effective lowering of
R0 and/or of effective herd immunity. Optionally, people are not
measured by actual spreading, but rather by characteristics and/or
behavior, which is expected to lead to greater spreading than others.
Optionally, such considerations also may be applied to below average in
expected spreading, however, such people usually have a smaller overall
effect on disease spread.
A
broad aspect of some embodiments of the invention relates to using a
prediction of individual behavior to decide on vaccination priority for
that individual. In some embodiments of the invention, such prediction
is based on past behavior of the individual. In some embodiments of the
invention, an individual is given a score used for prioritization. In
some embodiments of the invention, actual prioritization may be based on
a determination of the expected effect of such vaccination on spread of
disease. Optionally, this determination is using a simulation of
population disease spread. In some embodiments of the invention,
however, people are evaluated as individuals.
A
broad aspect of some embodiments of the invention relates to soft-fail
of vaccination prioritization, which avoids problems caused by imprecise
automated tracking methods. In some embodiments of the invention, the
use of imperfect information, which, on the one hand does not seriously
damage the quality of scoring and, on the other hand, can be used to
significantly increase privacy and/or ease of score collection is
provided. It is noted that a mistake, for example, of 4%, 8%, 15% or
intermediate percentages in score of an individual or missing a
potential super spreader will not have a significantly (e.g., a factor
of 2 or more) greater effect on a person (e.g., will not send such
person into quarantine) and/or the total efficacy of a vaccination
process. Also, even after such an effect, it is expected that the
overall result is better than naïve or general classification-based
vaccination prioritization. In some embodiments of the invention,
counting of contacts is allowed to be less precise. In some embodiments
of the invention, identification of the quality of the contacts (e.g.,
indoor/outdoor, coughing behavior, actual proximity and/or existence of
protective factors) is allowed to be reduced and optionally carried out
using less precise sensing means as provided, for example, by
cellphones. Optionally or additionally, the identification of unique
contacts is allowed to be less precise.
An
aspect of some embodiments of the invention relates to prioritizing
vaccinations and/or prophylactic treatments in a pandemic event by
identifying potential superspreaders. In some embodiments, potential
superspreaders are identified from a population before critical groups
have been excluded. In some embodiments, potential superspreaders are
identified from a population after critical groups have been excluded.
In some embodiments, critical groups are for example, health care
providers, essential service provides and high-risk individuals. In some
embodiments, potential superspreaders are identified according to one
or more of: their usual and/or expected level of activity, their usual
and/or expected type of activity, their usual and/or expected health
state, their belonging to a closed or open circle of connections, the
kind of individuals a certain subject usually meets, the kind of
individuals a certain subject has met and their actual sensed behavior.
In some embodiments, the entire population (with or without the critical
groups), or a part of the population, such as a critical group or other
group, are subjected to an analysis which provides each individual with
a “superspreader score” (referred hereinafter just as “score”) which
reflects a likelihood of such a person acting as a superspreader and/or
general expected ability of that person to spread the disease. In some
embodiments, potential superspreaders are identified according to a
score in relation to other scores from the rest of the population. In
some embodiments, potential superspreaders are identified according to a
score in relation to a predetermined score generated by the system. In
some embodiments, identified potential superspreaders having the highest
score are vaccinated (or provided with prophylactic treatments) first.
It should be appertained that the score may also be weighted with other
information, such as criticality for infrastructure, social standing
and/or risk form the disease or perceived risk to high-value members of
society.
An
aspect of some embodiments of the invention relates to prioritizing
vaccinations and/or prophylactic treatments in a pandemic event
according to a potential level of danger to the society. In some
embodiments, the invention relates identification of individuals that,
in case they were in a phase of infecting others with an infectious
disease/virus/pathogen, it would potentially put everyone else in
danger. For example, in the case where a subject is in potential contact
with other people and those other people potentially meet a high number
of individuals. For example, a subject that interacts face to face with
health provider personnel, but does not belong to the health provides
network. If that subject becomes infected, he/she can potentially infect
a high number of health provider personnel, which will then,
potentially, spread the infectious disease/virus/pathogen to a larger
population.
An
aspect of some embodiments of the invention relates to protecting the
privacy of individuals in a population when their information is used
for prioritizing vaccinations and/or prophylactic treatments in a
pandemic event, optionally also according to a potential level of danger
to the society. In some embodiments, actual names of individuals are
encrypted and/or anonymized in the system. In some embodiments, only a
device of an individual comprises the capabilities to translate between
the actual name of the individual and the encrypted/anonymized user
name. In some embodiments, the servers of the system comprise high
levels of protection and/or encryption for the information stored
therein. In some embodiments of the invention, even the device of the
user stores a minimum of identifiable information, such as a score, but
does not stores actual identities of persons met.
In
some embodiments of the invention, private information about a person's
activity and/or persons they came in contact with and/or geolocations
are maintained on that person's mobile device and used to determine a
priority for that person (e.g., by assessing the number of contacts and
overall risk of spreading disease due to typical behavior of that
person). Optionally, the mobile device is used to broadcast, optionally
in an anonymous manner, the score, so that, it may be determined, for
example, by a central computer, the distribution of scores across the
population. It should be noted that the actual identification of the
device and/or user is not needed, just the number of persons with each
score, so this can be taken into account together with number and/or
availability of vaccine doses, to plan a best dosing schedule.
Optionally, the mobile device will receive a predetermined scale of
scores from the system, which will be then used by the mobile device to
translate the score in view of the scale of scores and communicate the
user to get treatment and, optionally, the when and where.
In
some embodiments of the invention, once calculated, such dosing
schedule is broadcasted and each device can apply its score to the
schedule to determine a priority, which is given to the device owner.
Optionally, when arriving for a scheduled vaccination, the device owner
is required to show that code and, optionally, proof that the telephone
belongs to them.
In
one example, the local device calculates a score based on a user's
medical information and behavior. Optionally is also receives behavior
of those that person meets (e.g., transmitted to the device at
proximity/contact of devices of those people). In some embodiments, the
information is stored without identification of source, except possibly a
hash code, which, while can be used to detect that a certain device was
“met”, it cannot be used to identify the device. In some embodiments,
once this score (e.g., risk of contagion) is calculated, the broadcasted
information regarding number of vaccinations available and/or number of
persons in each class is noted. In some embodiments, this data may be
used to determine which vaccination priority the personal device score
merits, for example, in the same manner as would be by a central
computer (e.g., all scores above x, where there are y people with a
score above x and y is the number of available vaccines).
In
some embodiments of the invention, broadcasts and data transmissions
are digitally signed to prevent tampering. This has a potential
advantage of allowing more anonymous transmission method to be used
(e.g., Tor).
It
should be noted that additionally or alternatively to a central
processing, the calculation of the vaccine priority may be distributed
between some or all of the mobile devices, for example, using
parallelization methods known in the art, which optionally also prevent
significant amount of information from passing through any particular
device.
In
some embodiments of the invention, the device calculates the priority
and determines when the device owner should be vaccinated, treated
and/or tested. For example, the number and duration of persons in
proximity to the device can be used to calculate a risk score.
Optionally, medical information, such as susceptibility and/or risk of
spreading by coughing is downloaded to the device. This is typically not
a significant breach of anonymity, as the identity of the device is
typically known to the medical record provider. In some embodiments of
the invention, a person can apply to receive a rating, for example,
based on importance, job (e.g., healthcare provider), being part of
critical infrastructure and/or risk of death. Such rating may be
provided in the form of a one-time code, which the person can enter into
the device. In this manner, the device can increase or decrease the
risk score and/or priority of vaccination, without any central authority
being aware of the person's activities.
In
some embodiments of the invention, as the device calculates the
person's score, it may generate warning to the device owner to avoid or
reduce certain behavior. Optionally, such warning is tied to reduction
in priority if not heeded. Optionally or additionally, the manually
entered rating may affect such warnings. For example, socially
promiscuous activity by a doctor may not merit such warning and/or may
not reduce the doctor's score (at least while activity is performed at
an allowed location, such as a hospital, which location may be indicated
as part of the rating), but will generate a warning or a sanction
(e.g., if not heeded) to a person without such rating.
In
some embodiments of the invention, when deciding if to allow entry of a
person into a crowded location, such as a sports arena or a shopping
mall, a user may be required to show their rating.
A
potential benefit of some embodiments of the invention is that rather
than give out vaccination to critical workers, while placing the rest of
society in a lockdown (e.g., complete or semi or otherwise
restrictions), the total risk of spread may be reduced with a same or
smaller number of vaccine doses.
A
potential benefit of some embodiments of the invention is
self-policing. If a person does not install suitable software for
tracking movements, such person may receive a lower priority of
treatment/vaccination. Similarly, if a person leaves their device off,
then such off-time can be noted and used to affect the score, or even
can be used as an indication that that person is not at risk.
In some embodiments of the invention, a process of using the method includes:
(a)
Learning the behavior of individuals. This may be done, for example,
using existing contact tracking methods and/or using methods as
discussed herein. Optionally, such learned behavior is maintained in
privacy and/or collected in an anonymous manner or processed as it is
collected, to preserve anonymity.
(b)
Scoring, which can be based, for example, on number, variety and/or
quality of contacts, degree of bridging between subpopulations, risk to
individual, risk to others the individual is in contact with, other
facts that affect spreading (e.g., chronic cough) and/or existing
immunity.
(c) Inviting the individual to be vaccinated, optionally though software on an electronic device used for contact tracking.
(d) Vaccination, optionally verified using the software to identify the person being vaccinated.
An
aspect of some embodiments of the invention relates to identifying
potential superspreaders without the use of personal data. In some
embodiments, superspreaders are identified by providing an anonymous ID
to each individual, for example, when a dedicated application/software
(referred hereinafter as “application” or “app”) is installed in an
electronic device. In some embodiments, IDs are exchanged between
electronic devices when in proximity to each other (e.g., to indicate a
potentially infectious “meeting” of the device holders). In some
embodiments, what is transmitted is only a part of such ID (or an
indication thereof), which potentially decreases the chances to identify
the specific user. In some embodiments of the invention, even the
partial IDs substantially unique (e.g., a random number with more
possibilities than the number of expected meetings). In some embodiments
of the invention, the partial ID is selected to be non-unique, for
example, including only 100, 1000, 10,000 or intermediate or smaller or
greater possibilities. In some embodiments, prioritizing vaccinations
and/or prophylactic treatments in a pandemic event is performed
according to a superspreader score calculated by the number of IDs
collected by each user.
An
aspect of some embodiments of the invention relates to the quality of
people an individual meets. In some embodiments of the invention,
meeting with a person can be given a higher or lower weight, based on
whether that person is himself a super spreader and/or tends to meet
super spreaders and/or tends to meet others form many sub-populations.
In some embodiments of the invention, when two devices meet, they
exchange their own score and/or number of contacts or other information,
which is used to generate an indication of how much of a potential
superspreader that person is. In some embodiments, such people may be
given a higher weight. Optionally or additionally, persons who are from a
same subpopulation and/or which have fewer contacts and/or which are
met more often, are given a lower weight.
An
aspect of some embodiments of the invention relates to assessing the
degree of contacts inside a subpopulation and between subpopulations.
Society often has bubbles (subpopulations) within which there is a lot
of contact within the bubble but considerably less contact between
bubbles. In such a context, a person who bridges between bubbles may be a
greater threat of disease spread than a person with more overall
contacts but most or all within the bubble. In some embodiments of the
invention, a method is provided for assessing the degree to which a
person is within bubbles and/or bridges between bubbles or between
non-bubble subpopulations. For example, the method may be used to
distinguish between a first person where 90% of their contacts are
within a strongly connected sub-group vs. a person where only 10% of
their contacts are to a same strongly sub-group vs a person where 90% of
contacts are to a strongly connected sub-group, but there are multiple
(exclusive) such subgroups.
In
some embodiments of the invention, a distributed method of assessing
the degree to which contacts of an individual are within a strongly
connected or other type of bubble, is provided. An alternative view of
such method is assessing a degree of diffusion, which may be correlated
with a degree of propagation of disease.
In
some embodiments of the invention, some or all individuals are assigned
a second (or more) ID which is transferred to people they meet at a
probability lower than 100%. Optionally, when two individuals meet they
exchange not only their second ID, but also all second IDs they have
collected. As with a regular ID, the second or further IDs may be more
or less unique. When an individual device assesses the second IDs it
collected, it will tend to have fewer IDs if it is within a bubble
(e.g., because it will mainly have IDs within the bubble) than if it
interconnects bubbles (e.g., in which case it can have IDs from multiple
bubbles). Optionally, the number of second IDs is used as a measure of
diffusion of IDs in the contact network. In some embodiments of the
invention, the transfer of second IDs can be weighted (and/or
probability of transfer adjusted), for example, to better model the
likely of transfer of disease, for example, weighted higher for IDs
collected in closed spaces, at close distances or IDs received from a
device owned by a person with a chronic cough and/or less if owner is
known (e.g., recorded as such) to be careful with facemasks or other
protective gear. Such weighting may be used additionally or
alternatively also for the other scores described herein. The score may
be normalized to the period in which the score is collected. Such
normalization may be alternatively or additionally applied to score
based on the first ID. The normalization may be non-linear (e.g., the
score may increase faster at early times) and may be different for
different IDs and/or for different individual characteristic values.
In
some embodiments of the invention, the probability of transfer is
preset (e.g., 0.01%, 0.1%, 1%, 10% or intermediate or smaller or greater
percentages). Optionally or additionally, multiple additional IDs are
provided, each one transferred at a different probability. Optionally,
the preset probability is determined using a simulation. It is noted
that with a very small transfer probability, there may not be sufficient
diffusion of second ID values, while with a large probability, all
individuals will collect all second IDs, given enough time. For example,
a simulation of a contact network may be run with different preset
transfer values to detect a value which allows to distinguish between
typical sub-population sizes and/or which, within the measurement
period, does not reflect diffusion of substantially all second IDs all
over the network. Similarly, the degree of uniqueness of the second ID
may be selected using such a simulation to ensure that the probability
of a same second ID reaching an individual from two different subgroups
is sufficiently low (e.g., below 10%).
An
aspect of some embodiments of the invention relates to the political
issues involved in vaccination prioritization. In some embodiments of
the invention, using an objective measure of risk due to behavior allows
vaccination selection without (or less) a political fiat of selecting
groups and/or reducing political pressure applied to prefer a particular
group, as the individuals are treated by prioritization software as
individuals and do are not identified as or treated as belonging to
particular groups. Also within a particular group, using an automated
vaccination prioritization method can be used to reduce friction and
argument.
An
aspect of some embodiments of the invention relates to encouraging
users to use a dedicated application/software for tracking contacts (and
optionally identifying potential superspreaders either anonymized or
not) by providing vaccinations and/or prophylactic treatments first to
those individuals that use the dedicated software. In some embodiments,
individuals that use the dedicated software are those individuals that
contribute to the overall benefit of the population, therefore are
provided with vaccinations and/or prophylactic treatments before those
who not.
Before
explaining at least one embodiment of the invention in detail, it is to
be understood that the invention is not necessarily limited in its
application to the details of construction and the arrangement of the
components and/or methods set forth in the following description and/or
illustrated in the drawings and/or the Examples. The invention is
capable of other embodiments or of being practiced or carried out in
various ways.
Definition of the Population
During
a pandemic, once a valid vaccine/prophylactic drug becomes available,
and the number of vaccines/drug doses is limited or not all available at
the same time, the government must decide who will receive first the
vaccine/prophylactic treatment. According to studies, governments decide
to provide the first doses of the treatment to the group of individuals
that belong to:
a) Health care services, for example doctors, nurses, laboratories, hospitals, etc.;
b) Essential service services, for example police, fire fighters, public sector personnel, governmental personnel, etc.; and
c) High risk individuals, for example people with high risk of complications, pregnant women, children, etc.
These
individuals belong to a group called critical groups, due to the nature
of their activity or due to their health status during pandemic times.
Usually, critical groups amount to about 2% to about 10% of the total
population of a country.
After
the critical groups have been vaccinated and/or provided prophylactic
treatments, since the number of vaccinations/treatments is limited,
there is the question who should be vaccinated/treated next. This is
generally true also within a critical group or other group chosen for
vaccination, for example, a group of less at risk individuals, such as
males aged 50-60.
In
some embodiments, the population is defined as a number of individuals
between about 10 individuals and about 100 individuals, optionally
between about 100 individuals and about 1,000 individuals, optionally
between about 1,000 individuals and about 1,000,000 individuals,
optionally up to 10,000,000, optionally up to 100,000,000, optionally up
to the entire population of earth (e.g., 8 billion).
Principals of Herd Immunity
Before
explaining the invention, the notion of herd immunity should be
explained. Herd immunity (also called herd effect, community immunity,
population immunity, or social immunity) is a form of indirect
protection from infectious disease that occurs when a large percentage
of a population has become immune (resistant) to an infection, whether
through vaccination/prophylactic treatment or previous infections,
thereby providing a measure of protection for individuals who are not
immune. In a population in which a large proportion of individuals
possess immunity, such people being unlikely to contribute to disease
transmission, chains of infection are more likely to be disrupted, which
either stops or substantially slows the spread of disease. The greater
the proportion of immune individuals in a community, the smaller the
probability that non-immune individuals will come into contact with an
infectious individual, helping to shield non-immune individuals from
infection. Individuals can become immune by recovering from an earlier
infection or through vaccination/prophylactic treatment. Some
individuals cannot become immune because of medical conditions, such as
an immunodeficiency or immunosuppression, and for this group herd
immunity is a crucial method of protection. Once a certain threshold has
been reached, herd immunity gradually eliminates a disease from a
population. This elimination, if achieved worldwide, may result in the
permanent reduction in the number of infections to zero, called
eradication. For example, herd immunity created via
vaccination/treatment contributed to the eventual eradication of
smallpox in 1977 and has contributed to the reduction of the frequencies
of other diseases. Herd immunity does not apply to all diseases, just
those that are contagious, meaning that they can be transmitted from one
individual to another. Tetanus, for example, is infectious but not
contagious, so herd immunity does not apply. Herd immunity was
recognized as a naturally occurring phenomenon in the 1930s when it was
observed that after a significant number of children had become immune
to measles, the number of new infections temporarily decreased,
including among susceptible children. Mass vaccination/treatment to
induce herd immunity has since become common and proved successful in
preventing the spread of many infectious diseases. One of the main
problems with achieving herd immunity is that there might be a limited
number of vaccinations/treatments available to the population and mass
vaccination/treatment is either not possible or it would take a long
time to achieve herd immunity while the infectious disease continues to
spread.
It
is a potential benefit of some embodiments of the invention to provide a
method to resolve the problem of who to vaccinate/treat during a
pandemic when a low amount of vaccine/treatment doses are available,
while still providing an effective herd immunity, optionally by better
targeting those individuals likely to pass on disease and vaccinating at
least some of them, in a preferential manner.
Definition of Superspreaders
A
superspreader is an unusually contagious organism infected with a
disease (infectious disease/virus/pathogen). In the context of a
human-borne illness, a superspreader is an individual who is more likely
to infect others, compared with a typical infected person.
Some
cases of superspreading conform to the 80/20 rule, where approximately
20% of infected individuals are responsible for 80% of transmissions,
although superspreading can still be said to occur when superspreaders
account for a higher or lower percentage of transmissions. In epidemics
with such superspreader events (SSEV), the majority of individuals
infect relatively few secondary contacts.
Although
loose definitions of superspreader events exist, some effort has been
made at defining what qualifies as a superspreader event (SSEV).
Lloyd-Smith et al. (2005) define a protocol to identify a superspreader
event as follows:
a. estimate the effective reproductive number, R, for the disease and population in question;
b.
construct a Poisson distribution with mean R, representing the expected
range of Z due to stochasticity without individual variation;
c.
define an SSEV as any infected person who infects more than Z(n)
others, where Z(n) is the nth percentile of the Poisson(R) distribution.
This
protocol defines a 99th-percentile SSEV as a case, which causes more
infections than would occur in 99% of infectious histories in a
homogeneous population. For example, during the SARS-CoV-1 2002-2004
SARS outbreak from China, epidemiologists defined a superspreader as an
individual with at least eight transmissions of the disease.
Furthermore, superspreaders may or may not show any symptoms of the
disease. In the methods described here, a threshold (or scale) for being
a superspreader may be defined manually and/or determined by analyzing
actual contact-transmission data collected manually and/or
automatically.
Putting
aside hospitals, private residences and old-age homes, almost all of
these superspreader events (SSEVs) took place in the context of (1)
parties, (2) face-to-face professional networking events and meetings,
(3) religious gatherings, (4) sports events, (5) meat-processing
facilities, (6) ships at sea, (7) singing groups, and (8) funerals.
Factors of Transmission
Superspreaders
have been identified who excrete a higher than normal number of
pathogens during the time they are infectious. This causes their
contacts to be exposed to higher viral/bacterial loads than would be
seen in the contacts of non-superspreaders with the same duration of
exposure. This medical information may be available for at least some
individuals, for example, if the epidemic is a recurring one, such as
influenza. In addition, behavioral and medical attributes may also
increase infectivity. For example, a chronic cough (or one due to a
temporary disease, which may be noted in a person's medical record) may
increase the degree to which an individual is contagious. It is noted
that coughs and sneezes (and rate thereof) can be detected automatically
by a carried device, such as a cellphone, by signal analysis on an
automatically and optionally continually (or repeatedly discrete)
collected audio signal form the microphone. It is noted that an
individual's cellphone or other electronic device may have access to a
person medical records, by connecting to an EMR of that individual.
Basic Reproductive Number
The
basic reproduction number R0 is the average number of secondary
infections caused by a typical infective person in a totally susceptible
population. The basic reproductive number is found by multiplying the
average number of contacts by the average probability that a susceptible
individual will become infected, which is called the shedding
potential. The average number of contacts may further be weighed by
quality of contact (e.g., length, repetition, distance, protective means
and/or airflow quality)
R0=Number of contacts×Shedding potential
Individual Reproductive Number
R0=Number of contacts×Shedding potential
Individual Reproductive Number
The
individual reproductive number represents the number of secondary
infections caused by a specific individual during the time that
individual is infectious. Some individuals have significantly higher
than average individual reproductive numbers and are known as
superspreaders. Through contact tracing, epidemiologists have identified
superspreaders in measles, tuberculosis, rubella, monkeypox, smallpox,
Ebola hemorrhagic fever and SARS.
Co-Infections with Other Pathogens
Studies
have shown that men with HIV who are co-infected with at least one
other sexually transmitted disease, such as gonorrhea, hepatitis C, and
herpes simplex 2 virus, have a
higher HIV shedding rate than men without co-infection. This shedding
rate was calculated in men with similar HIV viral loads. Once treatment
for the co-infection has been completed, the HIV shedding rate returns
to levels comparable to men without co-infection. Therefore, it could be
hypothesized that in case of viral diseases transmitted through fluids,
people with other pathologies, like chronic coughing, could also be
defined as superspreaders and are optionally so defined, or weighted
accordingly in some embodiments of the invention.
Exemplary Pathogens
In
some embodiments, a pathogen may be one or more of a virus (in pl.
viruses), bacterium (bacteria), fungus (fungi) or a protozoan
(protozoa), for example coronavirus (COVID-19, SARS-CoV-1, SARS-CoV-2,
MERS-CoV). In some embodiments, the pathogen may be a virus, and said
virus is an influenza virus. In some embodiments, the disease results in
influenza like symptoms. It should be understood, that where referred
to “virus” and/or “pathogen”, any one of an “infectious disease”, a
“generic or specific pathogen”, a “generic or specific virus” are
included, and the use of the term “virus” and/or “pathogen” is just to
facilitate the explanation and they should include them.
In
some embodiments of the invention, the disease is transmitted by
respiratory means, for example, aerosol and/or droplets. Optionally, an
electronic device, such as a cellphone is used to detect contact which
may be sufficient to transmit (e.g., detecting proximity for example,
using Bluetooth power; detecting physical activity for example, buy
analysis of an audio trace recorded from such device; detecting being
indoors or outdoors based on geolocation or based on other sensors on
the cellphone that are affected by being indoors (e.g., echoes in
audio).
Vaccinations and Prophylactic Treatments
In
some embodiments, the term vaccination means the administration of a
vaccine to help the immune system develop protection from a disease. In
some embodiments, vaccines contain a microorganism or virus in a
weakened, live or killed state, or proteins or toxins from the organism.
In some embodiments, in stimulating the body's adaptive immunity, they
help prevent sickness from an infectious disease. In some embodiments,
as stated above, when a sufficiently large percentage of a population
has been vaccinated, herd immunity results.
In
some embodiments, the term prophylactic treatment means a preventive
measure taken to fend off a disease or another unwanted consequence.
In
order to facilitate the explanation of the invention, the term
“treatment” will be used. It should be understood that when the term
“treatment” is used it refers to both vaccinations and prophylactic
treatment.
In
some embodiments, vaccines are all compounds as disclosed in in the
website of the World Health Organization
(https://www[dot]who[dot]int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines),
which are all incorporated herein by reference, and which are
optionally provided (e.g., as a kit) with software such as described
herein and/or provided with instructions for use targeting potential
super spreaders detected, for example, using methods and apparatus as
described herein, and include the following:
28 candidate vaccines in clinical evaluation
| | |||||||
| COVID-19 | | | | | Route | | |
| Vaccine | | Type of | Number | Timing | of | Clinical | |
| developer/ | Vaccine | candidate | of | of | Admin- | Stage | |
| manufacturer | platform | vaccine | doses | doses | istration | Phase 1 | Phase 1/2 | Phase 2 | Phase 3 |
| | |||||||||
| University | Non- | ChAdOx1-S | 1 | | IM | | PACTR | 2020-001228-32 | ISR |
| of Oxford/ | Replicating | | | | | | 202006922165132 | | CTN |
| AstraZeneca | Viral | | | | | | 2020-001072-15 | | 89951424 |
| | Vector | | | | | | Interim | | |
| | | | | | | | Report | | |
| Sinovac | Inactivated | Inactivated | 2 | 0, 14 | IM | | NCT04383574 | | NCT |
| | | | | days | | | NCT04352608 | | 04456595 |
| Wuhan | Inactivated | Inactivated | 2 | 0, 14 or | IM | | Chi | | Chi |
| Institute of | | | | 0, 21 | | | CTR | | CTR |
| Biological | | | | days | | | 2000031809 | | 2000034780 |
| Products/ | | | | | | | | | |
| Sinopharm | | | | | | | | | |
| Beijing | Inactivated | Inactivated | 2 | 0, 14 or | IM | | Chi | | Chi |
| Institute of | | | | 0, 21 | | | CTR | | CTR |
| Biological | | | | days | | | 2000032459 | | 2000034780 |
| Products/ | | | | | | | | | |
| Sinopharm | | | | | | | | | |
| Moderna/ | RNA | LNP- | 2 | 0, 28 | IM | NCT | | NCT04405076 | NCT04470427 |
| NIAID | | encapsulated | | days | | 04283461 | | | |
| | | mRNA | | | | Interim | | | |
| | | | | | | Report | | | |
| BioNTech/ | RNA | 3 LNP- | 2 | 0, 28 | IM | | 2020-001038-36 | | NCT |
| FosunPharma/ | | mRNAs | | days | | | Chi | | 04368728 |
| Pfizer | | | | | | | CTR | | |
| | | | | | | | 2000034825 | | |
| CanSino | Non- | Adenovirus | 1 | | IM | Chi | | Chi | |
| Biological | Replicating | Type 5 | | | | CTR | | CTR | |
| Inc./Beijing | Viral | Vector | | | | 2000030906 | | 2000031781 | |
| Institute of | Vector | | | | | Study Report | | Study Report | |
| Biotechnology | | | | | | | | | |
| Anhui | Protein | Adjuvanted | 2 or 3 | 0, 28 | IM | NCT | | NCT | |
| Zhifei | Subunit | recombinant | | or | | 04445194 | | 04466085 | |
| Longcom | | protein | | 0, 28, | | | | | |
| Bio- | | (RBD- | | 56 days | | | | | |
| pharmaceutical/ | | Dimer) | | | | | | | |
| Institute of | | | | | | | | | |
| Microbiology, | | | | | | | | | |
| Chinese | | | | | | | | | |
| Academy | | | | | | | | | |
| of | | | | | | | | | |
| Sciences | | | | | | | | | |
| Institute of | Inactivated | Inactivated | 2 | 0, 28 | IM | NCT | NCT | | |
| Medical | | | | days | | 04412538 | 04470609 | | |
| Biology, | | | | | | | | | |
| Chinese | | | | | | | | | |
| Academy | | | | | | | | | |
| of Medical | | | | | | | | | |
| Sciences | | | | | | | | | |
| Inovio | DNA | DNA | 2 | 0, 28 | ID | | NCT | | |
| Pharma- | | plasmid | | days | | | 04447781 | | |
| ceuticals/ | | vaccine | | | | | NCT | | |
| International | | with | | | | | 04336410 | | |
| Vaccine | | electro- | | | | | | | |
| Institute | | poration | | | | | | | |
| Osaka | DNA | DNA | 2 | 0, 14 | IM | | NCT | | |
| University/ | | plasmid | | days | | | 04463472 | | |
| AnGes/ | | vaccine + | | | | | | | |
| Takara Bio | | Adjuvant | | | | | | | |
| Cadila | DNA | DNA | 3 | 0, 28, | lD | | CTRI/ | | |
| Healthcare | | plasmid | | 56 days | | | 2020/07/026352 | | |
| Limited | | vaccine | | | | | | | |
| Genexine | DNA | DNA | 2 | 0, 28 | IM | | NCT | | |
| Consortium | | Vaccine | | days | | | 04445389 | | |
| | | (GX-19) | | | | | | | |
| Bharat | Inactivated | Whole- | 2 | 0, 14 | IM | | NCT | | |
| Biotech | | Virion | | days | | | 04471519 | | |
| | | Inactivated | | | | | | | |
| Janssen | Non- | Ad26COVS1 | 2 | 0, 56 | IM | | NCT | | |
| Pharma- | Replicating | | | days | | | 04436276 | | |
| ceutical | Viral | | | | | | | | |
| Companies | Vector | | | | | | | | |
| Novavax | Protein | Full | 2 | 0, 21 | IM | | NCT | | |
| | Subunit | length | | days | | | 04368988 | | |
| | | recombinant | | | | | | | |
| | | SARS | | | | | | | |
| | | CoV-2 | | | | | | | |
| | | glycoprotein | | | | | | | |
| | | nanoparticle | | | | | | | |
| | | vaccine | | | | | | | |
| | | adjuvanted | | | | | | | |
| | | with | | | | | | | |
| | | Matrix M | | | | | | | |
| Kentucky | Protein | RBD- | 2 | 0, 21 | IM | | NCT | | |
| Bioprocessing, | Subunit | based | | days | | | 04473690 | | |
| Inc | | | | | | | | | |
| Arcturus/ | RNA | mRNA | | | IM | | NCT | | |
| Duke-NUS | | | | | | | 04480957 | | |
| Gamaleya | Non- | Adeno- | 1 | | IM | NCT | | | |
| Research | Replicating | based | | | | 04436471 | | | |
| Institute | Viral | | | | | NCT | | | |
| | Vector | | | | | 04437875 | | | |
| Clover | Protein | Native | 2 | 0, 21 | IM | NCT | | | |
| Biopharma- | Subunit | like | | days | | 04405908 | | | |
| ceuticals Inc./ | | Trimeric | | | | | | | |
| GSK/Dynavax | | subunit | | | | | | | |
| | | Spike | | | | | | | |
| | | Protein | | | | | | | |
| | | vaccine | | | | | | | |
| Vaxine Pty | Protein | Recombinant | 1 | | IM | NCT | | | |
| Ltd/Medytox | Subunit | spike | | | | 04453852 | | | |
| | | protein | | | | | | | |
| | | with | | | | | | | |
| | | Advax ™ | | | | | | | |
| | | adjuvant | | | | | | | |
| University | Protein | Molecular | 2 | 0, 28 | IM | ACTRN | | | |
| of | Subunit | clamp | | days | | 12620000674932p | | | |
| Queensland/ | | stabilized | | | | | | | |
| CSL/Seqirus | | Spike | | | | | | | |
| | | protein | | | | | | | |
| | | with | | | | | | | |
| | | MF59 | | | | | | | |
| | | adjuvant | | | | | | | |
| Institute | Replicating | Measles- | 1 or 2 | 0, 28 | IM | NCT | | | |
| Pasteur/ | Viral | vector | | days | | 04497298 | | | |
| Themis/ | Vector | based | | | | (not yet | | | |
| Univ. of | | | | | | recruiting) | | | |
| Pittsburg | | | | | | | | | |
| CVR/Merck | | | | | | | | | |
| Sharp & | | | | | | | | | |
| Dohme | | | | | | | | | |
| Imperial | RNA | LNP- | 2 | | IM | ISRCTN | | | |
| College | | nCoVsaRNA | | | | 17072692 | | | |
| London | | | | | | | | | |
| Curevac | RNA | mRNA | 2 | 0, 28 | IM | NCT | | | |
| | | | | days | | 04449276 | | | |
| People's | RNA | mRNA | 2 | 0, 14 | IM | Chi | | | |
| Liberation | | | | or 0, | | CTR | | | |
| Army | | | | 28 days | | 2000034112 | | | |
| (PLA) | | | | | | | | | |
| Academy | | | | | | | | | |
| of Military | | | | | | | | | |
| Sciences/ | | | | | | | | | |
| Walvax | | | | | | | | | |
| Biotech. | | | | | | | | | |
| Medicago | VLP | Plant- | 2 | 0, 21 | IM | NCT | | | |
| Inc. | | derived | | days | | 04450004 | | | |
| | | VLP | | | | | | | |
| | | adjuvanted | | | | | | | |
| | | with GSK | | | | | | | |
| | | or | | | | | | | |
| | | Dynavax | | | | | | | |
| | | adjs. | | | | | | | |
| Medigen | Protein | S-2P | 2 | 0 28 | IM | NCT | | | |
| Vaccine | Subunit | protein + | | days | | 04487210 | | | |
| Biologics | | CpG1018 | | | | | | | |
| Corporation/ | | | | | | | | | |
| NIAID/ | | | | | | | | | |
| Dynavax | |||||||||
| | |||||||||
139 candidate vaccines in preclinical evaluation
| |
|||||
| |
|
|
|
Current stage of | Same platform |
| |
Type of | |
|
clinical evaluation/ | for non- |
| |
candidate | |
Coronavirus | regulatory status- | Coronavirus |
| Platform | vaccine | Developer | target | Coronavirus candidate | candidates |
| |
|||||
| DNA | DNA, | DIOSynVax Ltd/ | SARS-CoV-2 | Pre-Clinical | |
| |
engineered | University of | and | ||
| |
vaccine inserts | Cambridge | SarbecoCoronaviruses | ||
| |
compatible | ||||
| |
with multiple | ||||
| |
delivery | ||||
| |
systems | ||||
| DNA | DNA vaccine | Ege University | SARS-CoV2 | Pre-Clinical | |
| DNA | DNA plasmid | Scancell/University | SARS-CoV2 | Pre-Clinical | |
| |
vaccine | of Nottingham/ | |||
| |
RBD&N | Nottingham Trent | |||
| |
|
University | |||
| DNA | DNA plasmid | National Research | SARS-CoV2 | Pre-Clinical | |
| |
vaccine | Centre, Egypt | |||
| |
S, S1, S2, | ||||
| |
RBD&N | ||||
| DNA | DNA with | Karolinska | SARS-CoV2 | Pre-Clinical | |
| |
electroporation | Institute/Cobra | |||
| |
|
Biologics | |||
| |
|
(OPENCORONA | |||
| |
|
Project) | |||
| DNA | DNA with | Chula Vaccine | SARS-CoV2 | Pre-Clinical | |
| |
electroporation | Research Center | |||
| DNA | DNA | Takis/Applied | SARS-CoV2 | Pre-Clinical | |
| |
|
DNA | |||
| |
|
Sciences/Evvivax | |||
| DNA | Plasmid DNA, | Immunomic | SARS-CoV2 | Pre-Clinical | SARS |
| |
Needle-Free | Therapeutics, | |||
| |
Delivery | Inc./EpiVax, | |||
| |
|
Inc./PharmaJet | |||
| DNA | DNA vaccine | BioNet Asia | SARS-CoV2 | Pre-Clinical | |
| DNA | msDNA | Mediphage | SARS-CoV2 | Pre-Clinical | |
| |
vaccine | Bioceuticals/University | |||
| |
|
of Waterloo | |||
| DNA | DNA vaccine | Entos | SARS-CoV2 | Pre-Clinical | |
| |
|
Pharmaceuticals | |||
| DNA | bacTRL-Spike | Symvivo | SARS-CoV2 | Pre-Clinical | |
| Inactivated | Inactivated + | KM Biologics | SARS-CoV2 | Pre-Clinical | JE, Zika |
| |
alum | ||||
| Inactivated | Inactivated | Selcuk University | SARS-CoV2 | Pre-Clinical | |
| Inactivated | Inactivated | Erciyes | SARS-CoV2 | Pre-Clinical | |
| |
|
University | |||
| Inactivated | Inactivated | National Research | SARS-CoV2 | Pre-Clinical | |
| |
whole virus | Centre, Egypt | |||
| Inactivated | Inactivated | Beijing Minhai | SARS-CoV2 | Pre-Clinical | |
| |
|
Biotechnology | |||
| |
|
Co., Ltd. | |||
| Inactivated | TBD | Osaka University/ | SARS-CoV2 | Pre-Clinical | |
| |
|
BIKEN/ | |||
| |
|
NIBIOHN | |||
| Inactivated | Inactivated + | Sinovac/Dynavax | SARS-CoV2 | Pre-Clinical | |
| |
CpG 1018 | ||||
| Inactivated | Inactivated + | Valneva/Dynavax | SARS-CoV2 | Pre-Clinical | |
| |
CpG 1018 | ||||
| Inactivated | Inactivated | Research Institute | SARS-CoV2 | Pre-Clinical | |
| |
|
for Biological | |||
| |
|
Safety Problems, | |||
| |
|
Rep of | |||
| |
|
Kazakhstan | |||
| Live | Codon | Mehmet Ali | SARS-CoV2 | Pre-Clinical | |
| Attenuated | deoptimized | Aydinlar | |||
| Virus | live attenuated | University/ | |||
| |
vaccines | Acibadem | |||
| |
|
Labmed | |||
| |
|
Health | |||
| |
|
Services A.S. | |||
| Live | Codon | Codagenix/Serum | SARS-CoV2 | Pre-Clinical | HAV, |
| Attenuated | deoptimized | Institute of | |
|
InfA, |
| Virus | live attenuated | India | |
|
ZIKV, |
| |
vaccines | |
|
|
FMD, |
| |
|
|
|
|
SIV, RSV, |
| |
|
|
|
|
DENV |
| Live | Codon | Indian | SARS-CoV2 | Pre-Clinical | |
| Attenuated | deoptimized | Immunologicals | |||
| Virus | live attenuated | Ltd/Griffith | |||
| |
vaccines | University | |||
| Non- | Sendai virus | ID Pharma | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | vector | ||||
| Vector | |||||
| Non- | Adenovirus- | Ankara | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | based | University | |||
| Vector | |||||
| Non- | Adeno- | Massachusetts | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | associated | Eye and | |||
| Vector | virus vector | Ear/Massachusetts | |||
| |
(AAVCOVID) | General | |||
| |
|
Hospital/AveXis | |||
| Non- | MVA encoded | GeoVax/BravoVax | SARS-CoV2 | Pre-Clinical | LASV, |
| Replicating Viral | VLP | |
|
|
EBOV, |
| Vector | |
|
|
|
MARV, |
| |
|
|
|
|
HIV |
| Non- | Replication | ReiThera/ | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | defective | LEUKOCARE/ | |||
| Vector | Simian | Univercells | |||
| |
Adenovirus | ||||
| |
(GRAd) | ||||
| |
encoding | ||||
| |
SARS-CoV-2S | ||||
| Non- | MVA-S | DZIF - | SARS-CoV2 | Pre-clinical | Many |
| replicating viral | encoded | German | |||
| vector | |
Center for | |||
| |
|
Infection | |||
| |
|
Research/IDT | |||
| |
|
Biologika | |||
| |
|
GmbH | |||
| Non- | MVA-S | IDIBAPS- | SARS-CoV2 | Pre-clinical | |
| replicating viral | |
Hospital | |||
| vector | |
Clinic, Spain | |||
| Non- | adenovirus- | Altimmune | SARS-CoV2 | Pre-Clinical | influenza |
| Replicating Viral | based | ||||
| Vector | NasoVAX | ||||
| |
expressing | ||||
| |
SARS2-CoV | ||||
| |
spike protein | ||||
| Non- | Adeno5-based | Erciyes | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | |
University | |||
| Vector | |||||
| Non- | 2nd Gen E2b- | ImmunityBio, | SARS-CoV2 | Pre-Clinical | flu, Chik, |
| Replicating Viral | Ad5 Spike, | Inc. & | |
|
Zika, |
| Vector | RBD, | NantKwest, | |
|
EBOV, |
| |
Nucleocapsid | Inc. | |
|
LASV, |
| |
Subcutaneous& | |
|
|
HIV/SIV, |
| |
Oral | |
|
|
Cancer |
| Non- | Ad5 S | Greffex | SARS-CoV2 | Pre-Clinical | MERS |
| Replicating Viral | (GREVAX ™ | ||||
| Vector | platform) | ||||
| Non- | Oral Ad5 S | Stabilitech | SARS-CoV2 | Pre-Clinical | Zika, |
| Replicating Viral | |
Biopharma | |
|
VZV, |
| Vector | |
Ltd | |
|
HSV-2 |
| |
|
|
|
|
and |
| |
|
|
|
|
Norovirus |
| Non- | adenovirus- | Valo | Pan-Corona | Pre-Clinical | |
| Replicating Viral | based + HLA- | Therapeutics | |||
| Vector | matched | Ltd | |||
| |
peptides | ||||
| Non- | Oral Vaccine | Vaxart | SARS-CoV2 | Pre-Clinical | InfA, |
| Replicating Viral | platform | |
|
|
CHIKV, |
| Vector | |
|
|
|
LASV, |
| |
|
|
|
|
NORV; |
| |
|
|
|
|
EBOV, |
| |
|
|
|
|
RVF, |
| |
|
|
|
|
HBV, |
| |
|
|
|
|
VEE |
| Non- | MVA | Centro | SARS-CoV2 | Pre-Clinical | Multiple |
| Replicating Viral | expressing | Nacional | |
|
candidates |
| Vector | structural | Biotecnología | |||
| |
proteins | (CNB-CSIC), Spain | |||
| Non- | Dendritic cell- | University of | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | based vaccine | Manitoba | |||
| Vector | |||||
| Non- | parainfluenza | University of | SARS-CoV2 | Pre-Clinical | MERS |
| Replicating Viral | virus 5 (PIV5)- | Georgia/ | |||
| Vector | based vaccine | University | |||
| |
expressing the | of Iowa | |||
| |
spike protein | ||||
| Non- | Recombinant | Bharat | SARS-CoV2 | Pre-Clinical | HeV, NiV, |
| Replicating Viral | deactivated | Biotech/Thomas | |
|
EBOV, |
| Vector | rabies virus | Jefferson | |
|
LASSA, |
| |
containing S1 | University | |
|
CCHFV, |
| |
|
|
|
|
MERS |
| Non- | Influenza A | National | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | H1N1 vector | Research | |||
| Vector | |
Centre, Egypt | |||
| Non- | Inactivated | National | SARS-CoV2 | Pre-Clinical | |
| Replicating Viral | Flu-based | Center for | |||
| Vector | SARS-CoV2 | Genetic | |||
| |
vaccine + | Engineering and | |||
| |
Adjuvant | Biotechnology | |||
| |
|
(BIOTEC)/GPO, | |||
| |
|
Thailand | |||
| Protein | Protein Subunit | Research | SARS-CoV2 | Pre-Clinical | |
| Subunit | |
Institute for | |||
| |
|
Biological | |||
| |
|
Safety | |||
| |
|
Problems, | |||
| |
|
Rep of | |||
| |
|
Kazakhstan | |||
| Protein | RBD-protein | Mynvax | SARS-CoV2 | Pre-Clinical | |
| Subunit | |||||
| Protein | Recombinant S | Izmir | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein | Biomedicine | |||
| |
|
and Genome | |||
| |
|
Center | |||
| Protein | Peptide + | Bogazici | SARS-CoV2 | Pre-Clinical | |
| Subunit | novel adjuvant | University | |||
| Protein | S subunit | University of | SARS-CoV2 | Pre-Clinical | |
| Subunit | intranasal | Virginia | |||
| |
liposomal | ||||
| |
formulation | ||||
| |
with | ||||
| |
GLA/3M052 | ||||
| |
adjs. | ||||
| Protein | S-Protein | Helix Biogen | SARS-CoV2 | Pre-Clinical | |
| Subunit | (Subunit) + | Consult, | |||
| |
Adjuvant, | Ogbomoso & | |||
| |
E coli based | Trinity | |||
| |
Expression | Immonoefficient | |||
| |
|
Laboratory, | |||
| |
|
Ogbomoso, | |||
| |
|
Oyo State, | |||
| |
|
Nigeria. | |||
| Protein | Protein Subunit | National | SARS-CoV2 | Pre-Clinical | |
| Subunit | S, N, M&S1 | Research | |||
| |
protein | Centre, | |||
| |
|
Egypt | |||
| Protein | Protein Subunit | University of | SARS-CoV2 | Pre-Clinical | |
| Subunit | |
San Martin | |||
| |
|
and | |||
| |
|
CONICET, | |||
| |
|
Argentina | |||
| Protein | RBD protein | Chulalongkorn | SARS-CoV2 | Pre-Clinical | |
| Subunit | fused with Fc | University/GPO, | |||
| |
of IgG + Adj. | Thailand | |||
| Protein | Capsid-like | AdaptVac | SARS-CoV2 | Pre-Clinical | |
| Subunit | Particle | (PREVENT- | |||
| |
|
nCoV | |||
| |
|
consortium) | |||
| Protein | Drosophila S2 | ExpreS2ion | SARS-CoV2 | Pre-Clinical | |
| Subunit | insect cell | ||||
| |
expression | ||||
| |
system VLPs | ||||
| Protein | Peptide | IMV Inc | SARS-CoV2 | Pre-Clinical | |
| Subunit | antigens | ||||
| |
formulated in | ||||
| |
LNP | ||||
| Protein | S protein | WRAIR/ | SARS-CoV2 | Pre-Clinical | |
| Subunit | |
USAMRIID | |||
| Protein | S protein + | National | SARS-CoV2 | Pre-Clinical | Influenza |
| Subunit | Adjuvant | Institute of | |||
| |
|
Infectious | |||
| |
|
Disease, | |||
| |
|
Japan/Shionogi/ | |||
| |
|
UMN Pharma | |||
| Protein | VLP- | Osaka | SARS-CoV2 | Pre-Clinical | |
| Subunit | recombinant | University/ | |||
| |
protein + | BIKEN/ | |||
| |
Adjuvant | National | |||
| |
|
Institutes of | |||
| |
|
Biomedical | |||
| |
|
Innovation, | |||
| |
|
Japan | |||
| Protein | microneedle | Univ. of | SARS-CoV2 | Pre-Clinical | MERS |
| Subunit | arrays S1 | Pittsburgh | |||
| |
subunit | ||||
| Protein | Peptide | Vaxil Bio | SARS-CoV2 | Pre-Clinical | |
| Subunit | |||||
| Protein | Adjuvanted | Biological E | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein subunit | Ltd | |||
| |
(RBD) | ||||
| Protein | Peptide | Flow Pharma | SARS-CoV2 | Pre-Clinical | Ebola, |
| Subunit | |
Inc | |
|
Marburg, |
| |
|
|
|
|
HIV, Zika, |
| |
|
|
|
|
Influenza, |
| |
|
|
|
|
HPV |
| |
|
|
|
|
therapeutic |
| |
|
|
|
|
vaccine, |
| |
|
|
|
|
BreastCA |
| |
|
|
|
|
vaccine |
| Protein | S protein | AJ Vaccines | SARS-CoV2 | Pre-Clinical | |
| Subunit | |||||
| Protein | Ii-Key peptide | Generex/EpiVax | SARS-CoV2 | Pre-Clinical | Influenza, |
| Subunit | |
|
|
|
HIV, |
| |
|
|
|
|
SARS-CoV |
| Protein | S protein | EpiVax/Univ. | SARS-CoV2 | Pre-Clinical | H7N9 |
| Subunit | |
of Georgia | |||
| Protein | Protein Subunit | EpiVax | SARS-CoV2 | Pre-Clinical | |
| Subunit | EPV-CoV-19 | ||||
| Protein | S protein | Sanofi | SARS-CoV2 | Pre-Clinical | Influenza, |
| Subunit | (baculovirus | Pasteur/GSK | |
|
SARS-CoV |
| |
production) | ||||
| Protein | gp-96 | Heat | SARS-CoV2 | Pre-Clinical | NSCLC, |
| Subunit | backbone | Biologics/Univ. | |
|
HIV, |
| |
|
Of Miami | |
|
malaria, |
| |
|
|
|
|
Zika |
| Protein | Peptide | FBRI SRC | SARS-CoV2 | Pre-Clinical | Ebola |
| Subunit | vaccine | VB | |||
| |
|
VECTOR, | |||
| |
|
Rospotrebnadzor, | |||
| |
|
Koltsovo | |||
| Protein | Subunit | FBRI SRC | SARS-CoV2 | Pre-Clinical | |
| Subunit | vaccine | VB | |||
| |
|
VECTOR, | |||
| |
|
Rospotrebnadzor, | |||
| |
|
Koltsovo | |||
| Protein | S1 or RBD | Baylor | SARS-CoV2 | Pre-Clinical | SARS |
| Subunit | protein | College of | |||
| |
|
Medicine | |||
| Protein | Subunit | iBio/CC- | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein, plant | Pharming | |||
| |
produced | ||||
| Protein | Recombinant | Saint- | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein, | Petersburg | |||
| |
nanoparticles | scientific | |||
| |
(based on S- | research | |||
| |
protein and | institute of | |||
| |
other epitopes) | vaccines and | |||
| |
|
serums | |||
| Protein | COVID-19 | Innovax/Xiamen | SARS-CoV2 | Pre-Clinical | HPV |
| Subunit | XWG-03 | Univ./GSK | |||
| |
truncated S | ||||
| |
(spike) proteins | ||||
| Protein | Adjuvanted | VIDO- | SARS-CoV2 | Pre-Clinical | |
| Subunit | microsphere | InterVac, | |||
| |
peptide | University of | |||
| |
|
Saskatchewan | |||
| Protein | Synthetic Long | OncoGen | SARS-CoV2 | Pre-Clinical | |
| Subunit | Peptide | ||||
| |
Vaccine | ||||
| |
candidate for S | ||||
| |
and M proteins | ||||
| Protein | Oral E. coli- | MIGAL | SARS-CoV2 | Pre-Clinical | |
| Subunit | based protein | Galilee | |||
| |
expression | Research | |||
| |
system of S | Institute | |||
| |
and N proteins | ||||
| Protein | Nanoparticle | LakePharma, | SARS-CoV2 | Pre-Clinical | |
| Subunit | vaccine | Inc. | |||
| Protein | Plant-based | Baiya | SARS-CoV2 | Pre-Clinical | |
| Subunit | subunit | Phytopharm/ | |||
| |
(RBD-Fc + | Chula | |||
| |
Adjuvant) | Vaccine | |||
| |
|
Research | |||
| |
|
Center | |||
| Protein | OMV-based | Quadram | SARS-CoV2 | Pre-Clinical | Flu A, |
| Subunit | vaccine | Institute | |
|
plague |
| |
|
Biosciences | |||
| Protein | OMV-based | BiOMViS | SARS-CoV2 | Pre-Clinical | |
| Subunit | vaccine | Srl/Univ. of | |||
| |
|
Trento | |||
| Protein | structurally | Lomonosov | SARS-CoV2 | Pre-Clinical | rubella, |
| subunit | modified | Moscow | |
|
rotavirus |
| |
spherical | State | |||
| |
particles of the | University | |||
| |
tobacco mosaic | ||||
| |
virus (TMV) | ||||
| Protein | Spike-based | University of | SARS-CoV2 | Pre-Clinical | Hepatitis C |
| Subunit | |
Alberta | |||
| Protein | Recombinant | AnyGo | SARS-CoV2 | Pre-Clinical | |
| Subunit | S1-Fc fusion | Technology | |||
| |
protein | ||||
| Protein | Recombinant | Yisheng | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein | Biopharma | |||
| Protein | Recombinant S | Vabiotech | SARS-CoV2 | Pre-Clinical | |
| Subunit | protein in IC- | ||||
| |
BEVS | ||||
| Protein | Orally | Applied | SARS-CoV2 | Pre-Clinical | |
| Subunit | delivered, heat | Biotechnology | |||
| |
stable subunit | Institute, | |||
| |
|
Inc. | |||
| Protein | Peptides | Axon | SARS-CoV2 | Pre-Clinical | |
| Subunit | derived from | Neuroscience | |||
| |
Spike protein | SE | |||
| Protein | Protein Subunit | MOGAM | SARS-CoV2 | Pre-Clinical | |
| Subunit | |
Institute for | |||
| |
|
Biomedical | |||
| |
|
Research, GC | |||
| |
|
Pharma | |||
| Protein | RBD-based | Neovii/Tel | SARS-CoV2 | Pre-Clinical | |
| Subunit | |
Aviv | |||
| |
|
University | |||
| Protein | Outer | Intravacc/Epivax | SARS-CoV2 | Pre-Clinical | |
| Subunit | Membrane | ||||
| |
Vesicle (OMV)- | ||||
| |
subunit | ||||
| Protein | Outer | Intravacc/Epivax | SARS-CoV2 | Pre-Clinical | |
| Subunit | Membrane | ||||
| |
Vesicle(OMV)- | ||||
| |
peptide | ||||
| Protein | Spike-based | ImmunoPrecise/ | SARS-CoV2 | Pre-Clinical | |
| Subunit | (epitope | LiteVax | |||
| |
screening) | BV | |||
| Replicating Viral | YF17D Vector | KU Leuven | SARS-CoV2 | Pre-Clinical | |
| Vector | |||||
| Replicating Viral | Measles Vector | Cadila | SARS-CoV2 | Pre-Clinical | |
| Vector | |
Healthcare | |||
| |
|
Limited | |||
| Replicating Viral | Measles Vector | FBRI SRC | SARS-CoV2 | Pre-Clinical | |
| Vector | |
VB | |||
| |
|
VECTOR, | |||
| |
|
Rospotrebnadzor, | |||
| |
|
Koltsovo | |||
| Replicating Viral | Measles Virus | DZIF - | SARS-CoV2 | Pre-clinical | Zika, |
| Vector | (S, N targets) | German | |
|
H7N9, |
| |
|
Center for | |
|
CHIKV |
| |
|
Infection | |||
| |
|
Research/ | |||
| |
|
CanVirex AG | |||
| Replicating Viral | Horsepox | Tonix | SARS-CoV2 | Pre-Clinical | Smallpox, |
| Vector | vector | Pharma/Southern | |
|
monkeypox |
| |
expressing S | Research | |||
| |
protein | ||||
| Replicating Viral | Live viral | BiOCAD and | SARS-CoV2 | Pre-Clinical | Influenza |
| Vector | vectored | IEM | |||
| |
vaccine based | ||||
| |
on attenuated | ||||
| |
influenza virus | ||||
| |
backbone | ||||
| |
(intranasal) | ||||
| Replicating Viral | Recombinant | FBRI SRC | SARS-CoV2 | Pre-Clinical | Influenza |
| Vector | vaccine based | VB | |||
| |
on Influenza A | VECTOR, | |||
| |
virus, for the | Rospotrebnadzor, | |||
| |
prevention of | Koltsovo | |||
| |
COVID-19 | ||||
| |
(intranasal) | ||||
| Replicating Viral | Attenuated | Fundação | SARS-CoV2 | Pre-Clinical | Influenza |
| Vector | Influenza | Oswaldo | |||
| |
expressing an | Cruz and | |||
| |
antigenic | Instituto | |||
| |
portion of the | Buntantan | |||
| |
Spike protein | ||||
| Replicating Viral | Influenza | University of | SARS-CoV2 | Pre-Clinical | |
| Vector | vector | Hong Kong | |||
| |
expressing | ||||
| |
RBD | ||||
| Replicating Viral | Replication- | IAVI/Merck | SARS-CoV2 | Pre-Clinical | Ebola, |
| Vector | competent | |
|
|
Marburg, |
| |
VSV chimeric | |
|
|
Lassa |
| |
virus | ||||
| |
technology | ||||
| |
(VSVΔG) | ||||
| |
delivering the | ||||
| |
SARS-CoV2 | ||||
| |
Spike (S) | ||||
| |
glycoprotein. | ||||
| Replicating Viral | VSV-S | University of | SARS-CoV2 | Pre-Clinical | HIV, |
| Vector | |
Western | |
|
MERS |
| |
|
Ontario | |||
| Replicating Viral | VSV-S | Aurobindo | SARS-CoV2 | Pre-Clinical | |
| Vector | |||||
| Replicating Viral | VSV vector | FBRI SRC | SARS-CoV2 | Pre-Clinical | |
| Vector | |
VB | |||
| |
|
VECTOR, | |||
| |
|
Rospotrebnadzor, | |||
| |
|
Koltsovo | |||
| Replicating Viral | VSV-S | Israel | SARS-CoV2 | Pre-Clinical | |
| Vector | |
Institute for | |||
| |
|
Biological | |||
| |
|
Research/Weizmann | |||
| |
|
Institute of | |||
| |
|
Science | |||
| Replicating Viral | M2-deficient | UW- | SARS-CoV2 | Pre-Clinical | influenza |
| Vector | single | Madison/FluGen/ | |||
| |
replication | Bharat | |||
| |
(M2SR) | Biotech | |||
| |
influenza | ||||
| |
vector | ||||
| Replicating Viral | Newcastle | Intravacc/ | SARS-CoV2 | Pre-Clinical | |
| Vector | disease virus | Wageningen | |||
| |
vector (NDV- | Bioveterinary | |||
| |
SARSCoV- | Research/ | |||
| |
2/Spike) | Utrecht Univ. | |||
| Replicating Viral | Avian | The | SARS-CoV2 | Pre-Clinical | |
| Vector | paramyxovirus | Lancaster | |||
| |
vector | University, | |||
| |
(APMV) | UK | |||
| RNA | Self- | Gennova | SARS-CoV2 | Pre-Clinical | |
| |
amplifying | ||||
| |
RNA | ||||
| RNA | mRNA | Selcuk | SARS-CoV2 | Pre-Clinical | |
| |
|
University | |||
| RNA | LNP-mRNA | Translate | SARS-CoV2 | Pre-Clinical | |
| |
|
Bio/Sanofi | |||
| |
|
Pasteur | |||
| RNA | LNP-mRNA | CanSino | SARS-CoV2 | Pre-Clinical | |
| |
|
Biologics/Precision | |||
| |
|
NanoSystems | |||
| RNA | LNP- | Fudan | SARS-CoV2 | Pre-Clinical | |
| |
encapsulated | University/ | |||
| |
mRNA | Shanghai | |||
| |
cocktail | JiaoTong | |||
| |
encoding VLP | University/ | |||
| |
|
RNACure | |||
| |
|
Biopharma | |||
| RNA | LNP- | Fudan | SARS-CoV2 | Pre-Clinical | |
| |
encapsulated | University/ | |||
| |
mRNA | Shanghai | |||
| |
encoding RBD | JiaoTong | |||
| |
|
University/ | |||
| |
|
RNACure | |||
| |
|
Biopharma | |||
| RNA | Replicating | Centro | SARS-CoV2 | Pre-Clinical | |
| |
Defective | Nacional | |||
| |
SARS-CoV-2 | Biotecnología | |||
| |
derived RNAs | (CNB-CSIC), Spain | |||
| RNA | LNP- | University of | SARS-CoV2 | Pre-Clinical | MERS |
| |
encapsulated | Tokyo/Daiichi- | |||
| |
mRNA | Sankyo | |||
| RNA | Liposome- | BIOCAD | SARS-CoV2 | Pre-Clinical | |
| |
encapsulated | ||||
| |
mRNA | ||||
| RNA | Several mRNA | RNAimmune, Inc. | SARS-CoV2 | Pre-Clinical | |
| |
candidates | ||||
| RNA | mRNA | FBRI SRC | SARS-CoV2 | Pre-Clinical | |
| |
|
VB | |||
| |
|
VECTOR, | |||
| |
|
Rospotrebnadzor, | |||
| |
|
Koltsovo | |||
| RNA | mRNA | China | SARS-CoV2 | Pre-Clinical | |
| |
|
CDC/Tongji | |||
| |
|
University/Stermina | |||
| RNA | LNP-mRNA | Chula | SARS-CoV2 | Pre-Clinical | |
| |
|
Vaccine | |||
| |
|
Research | |||
| |
|
Center/University | |||
| |
|
of Pennsylvania | |||
| RNA | mRNA in an | eTheRNA | SARS-CoV2 | Pre-Clinical | |
| |
intranasal | ||||
| |
delivery | ||||
| |
system | ||||
| RNA | mRNA | Greenlight | SARS-CoV2 | Pre-Clinical | |
| |
|
Biosciences | |||
| RNA | mRNA | IDIBAPS- | SARS-CoV2 | Pre-Clinical | |
| |
|
Hospital | |||
| |
|
Clinic, Spain | |||
| VLP | VLP | Bezmialem | SARS-CoV2 | Pre-Clinical | |
| |
|
Vakif | |||
| |
|
University | |||
| VLP | VLP | Middle East | SARS-CoV2 | Pre-Clinical | |
| |
|
Technical | |||
| |
|
University | |||
| VLP | Enveloped | VBI | SARS-CoV-2, | Pre-Clinical | CMV, |
| |
Virus-Like | Vaccines Inc. | SARS-CoV, & | |
GBM, Zika |
| |
Particle | |
MERS-CoV | ||
| |
(eVLP) | ||||
| VLP | S protein | IrsiCaixa | SARS-CoV2 | Pre-Clinical | |
| |
integrated in | AIDS | |||
| |
HIV VLPs | Research/IRTA- | |||
| |
|
CReSA/Barcelona | |||
| |
|
Supercomputing | |||
| |
|
Centre/Grifols | |||
| VLP | VLP + | Mahidol | SARS-CoV2 | Pre-Clinical | |
| |
Adjuvant | University/The | |||
| |
|
Government | |||
| |
|
Pharmaceutical | |||
| |
|
Organization | |||
| |
|
(GPO)/Siriraj | |||
| |
|
Hospital | |||
| VLP | Virus-like | Navarrabiomed, | SARS-CoV2 | Pre-Clinical | |
| |
particles, | Oncoimmunology | |||
| |
lentivirus and | group | |||
| |
baculovirus | ||||
| |
vehicles | ||||
| VLP | Virus-like | Saiba GmbH | SARS-CoV2 | Pre-Clinical | |
| |
particle, based | ||||
| |
on RBD | ||||
| |
displayed on | ||||
| |
virus-like | ||||
| |
particles | ||||
| VLP | ADDomerTM | Imophoron | SARS-CoV2 | Pre-Clinical | |
| |
multiepitope | Ltd and | |||
| |
display | Bristol | |||
| |
|
University's | |||
| |
|
Max Planck | |||
| |
|
Centre | |||
| VLP | Unknown | Doherty | SARS-CoV2 | Pre-Clinical | |
| |
|
Institute | |||
| VLP | VLP | OSIVAX | SARS-CoV1 | Pre-Clinical | |
| |
|
|
SARS-CoV2 | ||
| VLP | eVLP | ARTES | SARS-CoV2 | Pre-Clinical | malaria |
| |
|
Biotechnology | |||
| VLP | VLPs | Univ. of Sao | SARS-CoV2 | Pre-Clinical | |
| |
peptides/whole | Paulo | |||
| |
virus | ||||
| |
|||||
In
some embodiments, vaccines are all compounds as disclosed in in the
website of ClinicalTrials.gov
(https://clinicaltrials[dot]gov/ct2/results?cond=COVID-19), which are
all incorporated herein by reference. Other vaccines may be used as
well.
In some embodiments, treatment can be the use of Hydroxychloroquine and azithromycin plus zinc.
In some embodiments, vaccines include the vaccine developed by the Moscow-based Gamaleya Institute, named Sputnik-V.
In
some embodiments, providing a treatment as disclosed above to healthy
subjects can be understood as prophylactic treatment and/or vaccination.
Exemplary Classification of Superspreader
Referring now to FIG. 1,
showing a schematic representation of a definition of superspreader,
according to some embodiments of the invention. In addition to the
notion that a superspreader might be identified as a person who excretes
a higher than normal number of pathogens during the time they are
infectious, a superspreader is a person who may excrete a normal (or
low) number of pathogens during the time they are infectious but this
person is potentially and/or effectively in contact with a high number
of people, therefore potentially infecting the same or more number of
people as a person who excretes a higher than normal number of
pathogens, as schematically shown for example in FIG. 1.
Following this logic, according to some embodiments of the invention, a
superspreader is further identified according to the number of people
he/she can potentially be in contact with, is expected or estimated to
be in contact with (e.g., based on number he has been in contact with),
no matter the level of excretion of said person.
Super-Spreading Potential Score
In
accordance with some embodiments of the invention, there are provided
methods and systems of providing subjects in a population with a
“superspreading score”, which will help to provide the order in which
the subjects, optionally in groups of subjects, will receive treatments.
In some embodiments, the higher the score the higher the potential of
each individual to spread the disease. In some embodiments, the higher
the score, the earlier the individual should receive the treatments. In
some embodiments, a potential advantage of vaccinating/treating
individuals having the higher superspreading score is to block potential
intersections where a higher number of individuals might be infected by
the potential superspreaders, and this is done by vaccinating
individuals with potentially and/or actual higher chances to meet other
people, and optionally also in relation to other individuals (for
example by normalization of the data). In some embodiments, a potential
advantage of this method is that a population will potentially reach
faster a state of herd immunity, as the provision of treatments
continues.
Referring now to FIG. 2,
showing a flowchart of an exemplary embodiment of the invention. In
some embodiments, the system and methods are based on the following:
receiving information about a subject 202, analyzing the received information 204, generating a score 206 based on the information, optionally allocating the subject based to the score to a score group 208, and providing treatment according to the score and/or according to the score group 210. As will be shown below, some or all of the receiving and generating may be performed on an electronic device of subject 202.
Exemplary Factors Influencing the Score
In
some embodiments, the score is generated utilizing one or more factors
and/or components, each influencing the final score by either adding or
subtracting from the score. In some embodiments, the one or more factors
can influence the score in a linear matter (increasing/decreasing the
score linearly, for example +1 to the score or −2 to the score) and/or
one or more factor can affect the score in a weighted matter, as will be
further explained below. Exemplary factors and/or components are one or
more of the following:
Profession in Record of the Individual
In
some embodiments, the profession of the individual is correlated with a
potential number of people the person might be in contact with during a
regular day of operation. In some embodiments, individuals that
potentially must meet many people due to their profession will receive a
high score. For example, cashiers at the supermarket, vendors in
markets, bus drivers, delivery people, technicians, librarians, etc. In
some embodiments of the invention, the profession information is used to
estimate a contact quality score, for example, doctors being more
careful with PPE than teachers. It is a particular feature of some
embodiments of the invention, that differences within such a group, such
as between different doctors, are determined. In some embodiments of
the invention, a subject's score is modified according to the
profession, for example, to compensate for criticality of the subject
and/or to lack of control of the subject (e.g., a bus driver) over
number of contacts.
In
some embodiments of the invention, a subject provides profession
information or other information used to adjust scoring by scanning a
barcode (or other machine-readable item such as a barcode or RFID chip
identity card) which is optionally digitally signed with such
information. Optionally, this allows the device to know the profession
information, but may not allow the device and/or the information
provider to link the request for data to a particular individual. Thus
potentially maintaining privacy.
Characteristics of Population Potentially to Meet
In
some embodiments, the kind of population that a certain subject can
potentially meet will either increase or decrease the score. For
example, teachers that meet many children will be provided with a higher
score, since if and once the children are infected by the teacher, the
children return home and potentially infect their families. While for
example, a doctor that works at a prison would potentially receive a
lower score since the incarcerated people in the prison are not leaving
and probably will not infect anyone else (the infection is contained to
the prison alone).
Another
example, if a certain subject meets only a certain number of
individuals, and mainly only those individuals, for example a subject in
a close community, then that subject will receive a lower score.
Characteristics of Population that a Subject Actually Met
In
some embodiments, if a certain subject meets people that were
identified as superspreaders, this will influence the score by
increasing their score, also when compared to subjects that do not meet
superspreaders and/or regular people. In some embodiments, the
information regarding meeting a superspreader is performed between the
mobile devices in an anonymous matter, for example, as will be further
explained below.
The Nature of the Locations
In
some embodiments, the nature of a location means if it is in a closed
place, if it is in an open space, if it is indoors, if it is outdoors,
quality of ventilation or any combination thereof. In some embodiments,
the nature of the locations can drastically change the score given to a
subject. It has been shown that a likelihood of a subject transmitting a
pathogen increases by a factor of between about 10 times to about 100
times when the location is indoors and/or in a closed space. This is
because the risk of infection is increased due to the possible buildup
of the airborne pathogen-carrying droplets, the pathogen likely higher
stability in indoor air, and/or a larger density of people.
In
some embodiments, if the location is indoors or in a closed location,
then the score given to the subject for a contact will increase.
In
some embodiments, other factors that influence the increment or
reduction of the likelihood of a subject transmitting a pathogen indoors
are one or more of ventilation rate, use of natural ventilation,
avoidance of air recirculation and use of air filters.
In
some embodiments, the system will comprise information on indoor
locations related to the ventilation rate, use of natural ventilation,
avoidance of air recirculation and use of air filters. In some
embodiments, an indoor place comprising a high ventilation rate score
will provide a lower score to the individual when compared to a place
having a low ventilation rate score.
The Kind of Places Usually Visited by the Subject
In
some embodiments, subjects that are prone to frequent religious or
secular events, like in a synagogue, a church or a mosque or a dancing
venue, where the people are in close proximity to each other, and talk,
pray, sing and/or breathe deeply and/or mingle more, will receive a
higher score (e.g., for such a contact event) than those who do not
frequent religious events. In some embodiments, similarly to above, also
subject that are prone to frequent sports events will receive a higher
score. In some embodiments, places that are frequented regularly by a
large quantity of individuals (including public transportation,
detectable for example, by geolocation and/or regular start-stop
movement that matches a public transportation profile and/or base don
payment activity using the tracking electronic device) will be marked as
points on interest for the potential spreading of the infectious
disease/virus/pathogen, and subjects that frequent those places will
receive a higher score.
The Length of Time at the Locations
In
some embodiments, the length that a subject stays in one place will
contribute to the determination of the probability to infect others
and/or to be infected by others. For example, a subject that visits many
places but stays there just for a minute or two might receive a lower
score (e.g., for a contact event) than a person that stays for longer in
a few places, since staying longer at one place potentially increases
the chances to infect and/or be infected.
Historical Geolocation Data of the Individual
In
some embodiments, historical data of the location of an individual is
used to assess the potential geolocation activity of that specific
individual. For example, Google Maps® data saved in servers, Waze® data
saved in servers, and other geolocation applications configured to save
geolocation activity data. In some embodiments, individuals having a
high volume of movement data (and/or high usage of public
transportation) in their historical geolocation data will receive a high
score. In some embodiments, the historical data is used to further
assess a reliability of change in behavior of a subject, for example to
determine if to increase score in cases where the actual geolocation
data changes drastically (for example if there is a risk that a subject
wants a higher score to receive the vaccine and increases his movements
to achieve so).
Actual Geolocation Data of the Individual
In
some embodiments, actual measured geolocation data of each individual
is monitored to assess their potential to meet other people. In some
embodiments, people which show high number movements during the day in
areas where other people are located will receive a high score. In some
embodiments, actual geolocation data of each individual is monitored
using one or more of:
1. Electronic devices, for example the location provided by the GPS of their own cellphones;
2.
Using face recognition technology based on one or more of: a) video
surveillance data received from available sources, for example street
cameras, ATM's, private surveillance cameras in stores, buildings and
houses, etc.; b) social media.
3.
Digital activity, for example credit card usage, IP address used while
using a computer or an electronic device, antennas that receive data
while performing a phone call.
Optionally
or additionally, such actual geolocation data is used instead of or in
addition to actually identifying contact between people.
Historical Medical Data of the Individual
In
some embodiments, historical medical data of each individual is
assessed to provide a score. For example, as mentioned above,
individuals with chronic coughing will receive a high score since they
have potentially a higher chance to transmit the infectious
disease/virus/pathogen. In some embodiments, individuals having a
background condition that enhances the chances of transmitting the
disease will receive a high score.
Actual Medical Data of the Individual
In
some embodiments, during the pandemic, every new medical data
concerning each individual is monitored to assess if the new data
indicates a change in the medical status of the individual regarding
their potential to infect others. Using the example above, if a person
is diagnosed with chronic coughing it will increase their score (e.g.,
in general and/or per contact).
Third Party Information Regarding the Individual
In
some embodiments, third party information from individuals informing on
others will be assessed to decide if the information needs to affect
the score. For example, if a third party informs that a person that
showed low movement data and received a low score is actually performing
many movements, once the information is verified, the score will change
accordingly. The contrary is also valid, for example, a third party
informed that a person that showed high movement data and received a
high score is actually staying at home, once the information is
verified, the score may change accordingly.
Dedicated Mandatory App
In
some embodiments, in view of the pandemic, the government may order the
citizens to install a dedicated application on their smartphones (or
other smart devices like tablets, smart watches, smart glasses, etc.) to
help the government with the logistics of the vaccination procedures.
In some embodiments, the government (or other body) provides the public
with such dedicated smart devices. In some embodiments, the app and/or
the smart device is configured to inform on the user's location at all
times and to communicate with adjacent smart devices (via Bluetooth for
example) to assess the interactions between users, for example vicinity
between users, movement of users, etc.). In some embodiments of the
invention, already existing software may be used, for example, both
android and is based cellphones have software (e.g., as an operating
system service) which can detect proximity of others and such software
may be used or improved to provide functionality as described herein.
In
some embodiments, such app can be used to provide information regarding
how many unique people the user meets. For example, a certain user can
meet many people but they are all the same people all the time. While
another user can meet fewer people but each one is a different
individual. In some embodiments, the second user may potentially receive
a higher score and therefore receive treatment first. In some
embodiments, such app and/or smart devices are also used to assess the
progression of the vaccination procedures and the efficacy of the
vaccination procedure. In some embodiments, individual data arriving
from each user is coupled with their health information (sick,
vaccinated, recovered, etc.) to further assess the progression of the
vaccination procedures and the efficacy of the vaccination procedure.
Optionally, if the persons met by a user are vaccinated or otherwise
determined to be immune, such contacts may not count and/or be weighted
lower.
In
some embodiments, the app will be also used to send personalized
communication to the users, for example, to come and be vaccinated. In
some embodiments, in view of the information received from the app,
specific actions are taken, for example, send a communication to the
user to enhance his awareness to behavioral rules during pandemic, to
come and be vaccinated, to avoid certain locations, which are at high
risk of contagion.
Dedicated Voluntary App
In
some embodiments, in view of the pandemic, the population is encouraged
to install a dedicated app, where those that do install the app are
rewarded. In some embodiments, the reward is priority to receive
treatment.
Monitoring Behavior of Subject
In
some embodiments, the behavior of the subject is monitored in relation
to safety features performed by the subject, for example, wearing a mask
(e.g., analyzing images taken during calls or other looking at screen
of cellphone), washing his hands (e.g., analyzing sounds of water
running or movement by a smartwatch), keeping social distancing (e.g.,
based on Bluetooth power levels and/or NFC detection), moving between
multiple locations, etc. In some embodiments, these are monitored using
the same devices/methods as disclosed above.
Exemplary Scoring Method
In
some embodiments, each individual in a population (e.g., above 100,
1000, 10000 and/or 100000 individuals) is provided with a score defining
the potential level of superspreading of each individual. In some
embodiments, scores are defined as number of contacts (see herein), and
the number of contacts that are counted are from about 10 to about 100,
optionally from about 100 to about 1000, optionally from about 1000 to
about 10000, for example 100, 400, 1000, 2000, 10000 or intermediate or
greater numbers. In some embodiments, a high score defines a high
potential of superspreading, while a low score defines a low potential
of superspreading. In order to facilitate the explanations of the
invention, a scoring scale from 0 to 100 will be used. It should be
understood that other scales can be used, like heat-map scoring, decimal
order scales, etc., all of which are included in the scope of the
invention. In some embodiments of the invention, the score is open
ended. In some embodiments of the invention, the score is normalized,
for example, to other scores. The normalization need not be linear. In
some embodiments of the invention, the score is a scalar. In some
embodiments of the invention, the score is multi-dimensional, for
example, including a superspreader potential dimension and a variability
in behavior dimension)
In
some embodiments, the score is calculated using weighted scoring
models, in which one or more factors and/or components are assessed
according to the received information data. Referring now to FIG. 3,
showing a schematic flowchart of a method of calculating a weighted
score, according to some embodiments of the invention. In some
embodiments, the system receives information data about a subject 302. In some embodiments, the information data is divided according to the source of the information data 304, for example, electronic information 306 from smartphones, cameras, credit card information, etc., geographical information 308, for example from GPS or cell towers, governmental information 310, for example from the census bureau or EMR (electronic medical records), human information 312,
for example from other individuals calling an providing the information
about other individuals, and one or more of the factors and/or
components disclosed above. In some embodiments, the system then
calculates a weighted score of each information, optionally according to
a predetermined criterion 314. In some embodiments, the system then generates a total score from the different weighted scores, optionally according to a predetermined criterion 316.
In some embodiments, the system then provides a list comprising an
order of treatment, which is then used to actually treat the population 318.
In
some embodiments, the score comprises a plurality of components, for
example predicted likelihood of a subject transmitting an infectious
disease/virus/pathogen, predicted likelihood of a subject contracting an
infectious disease/virus/pathogen, relative health risk to a subject if
said subject contracts a infectious disease/virus/pathogen, damage to
society if the subject contracts a infectious disease/virus/pathogen;
one or more of the above optionally in view of physical proximity data
to other subjects.
In some embodiments, physical proximity data of a subject with other subjects is calculated by including one or more of:
1. The number of subjects the subject potentially is in contact with;
2. The potential and/or actual distance of the subject to the other subjects;
3. The time length of the potential and/or actual encounter of the subject with the other subjects.
In
some embodiments of the invention, the score is updated for and/or
after each contact event. In some embodiments of the invention, update
is at end of the day, which may allow aggregating multiple meetings with
a same person. Optionally or additionally, the score is updated per a
set of contact events. In some embodiments of the invention, the score
is calculated after all contact events are collected, for example, based
on an analysis of a contact-network to identify individuals, which, if
vaccinated, will best stop infection. Such analysis may be carried out
by simulating the contact network and trying out various vaccination
schemes and/or removal of various individuals and/or sets of
individuals.
From Score to Treatment
In
some embodiments, once the scoring of each individual is achieved, or
optionally the scoring of a high number of individuals of the
population, a list is created having the order in which each individual
will receive the treatment. In some embodiments, the list is optionally
divided by groups, for example, all the individuals that scored between
100 and 90 are grouped in group A, which will receive first the
treatments. Then all the individuals that scored between 90 and 80 are
grouped in group B, which will receive second the treatments, and so on.
Informing the Public
In
some embodiments, once the list is made, individuals will be informed
on when and where to go and receive the treatments, for example, by
means of emails, dedicated apps in their cellphones, over the media,
etc.
Exemplary Simulations
In
some embodiments, models and simulations are run in dedicated
computers, for example, to assess the potential progression of the
treatments and the probable time to reach herd immunity and/or select
values for various parameters. In some embodiments, simulations include
the insertion of one or more of actual data received from individuals,
simulated data of/from individuals (in case is necessary to run probable
scenarios). In some embodiments, evaluations and models utilize one or
more of neural networks, machine learning and dedicated simulations.
In
some embodiments, the simulations take under consideration and model
the probability of the treatments to work (or not work) on the
individual.
In
some embodiments, the simulations take under consideration and model
the kind of population that a certain subject can potentially meet and
the potential population those individuals will potentially meet
afterwards. For example, teachers that meet many children will be
provided with a higher simulated score, since if and once the children
are infected by the teacher, the children return home and potentially
infect their families. While for example, a doctor that works at a
prison would potentially receive a lower simulated score since the
incarcerated people in the prison are not leaving and probably will not
infect anyone else (the infection is contained to the prison alone).
In
some embodiments, simulations are performed to evaluate parameter
values used to identify a superspreader and possibly how to
differentiate them from regular individuals.
Exemplary Spreading Network
In some embodiments, before, during and/or receiving the information regarding the individuals in the whole population, a network 400 of the population is created, as shown for example in FIG. 4.
In some embodiments, the network is constantly updated by the system.
In some embodiments, the network is used to determine the potential
spreading of the infectious disease/virus/pathogen if a certain
individual is infected. In some embodiments, when possible, clusters in
the network are identified, for example clusters 402 through 412 in network 400.
In some embodiments, when evaluating whom to provide treatments, the
system assesses the individuals in the clusters and performs analysis
and simulations to choose which individuals to treat (e.g., individuals
that interconnect clusters). In some embodiments, this is performed in
addition to the scoring performed and generated on each single
individual. For example, it can be seen that individual 414 belongs to both clusters 402 and 404, thereby creating a potential bottleneck (or bridge) between clusters. Therefore, it would be advantageous to treat individual 414 to protect cluster 404 from potential infections coming from cluster 402. Same logic is applied to individual 416. Treating individual 416 can potentially protect clusters 410 and 412 from potential infections coming from cluster 402.
In some embodiments, the system identifies potential key individuals
and/or potential key groups of individuals to treat first in order to
potentially protect clusters of individuals. In some embodiments, the
systems performs this assessment in view of the number of doses
available to the population. For example, if there is a large number of
doses, instead of treating the individuals located in the bottlenecks,
it might be advantageous to treat first all individuals in the cluster 402, thereby potentially protecting the rest of the clusters from infection.
In
one example, the system selectively removes individuals to identify
which set of N individuals (e.g., where N is the number of doses to be
used) is best to remove. This can be done using brute force approaches,
e.g., of trying a plurality of sets. Optionally or additionally, this is
done by selecting sets of individuals (e.g., based on some shared
characteristic, such as profession or place in the network) and seeing
the effect of vaccinating these individuals. Optionally or additionally,
a different search technique is used, e.g., treating the problem as an
optimization problem.
Exemplary Use of the System and Methods for Testing
In
some embodiments, the system and methods are used to identify selected
subjects to be tested for the disease. In some embodiments, the testing
is used to assess one or more of the progress of the disease, the
progress of the treatments, the progress of the herd immunity, etc.
Exemplary Use of the System and Methods for Determining Who Will Receive a Certain Type of Vaccination
In
some embodiments, during the development of vaccines for a certain
disease, different vaccines comprising different vaccine potencies are
developed. In some embodiments, vaccine potency is a quantitative
measure of the specific ability of the vaccine product to achieve an
intended biological effect defined in a suitable biological assay based
on the attribute of the product that is linked to the relevant
biological properties. In some embodiments, the system is used to
identify which individuals will receive which types of vaccines in
relation to their potency. For example, individuals that received and/or
were identified as a high superspreading score by the system would be
vaccinated with more potent vaccines, when compared with other
individuals having lower superspreading scores. In some embodiments,
those individuals having lower superspreading scores might either
receive later a vaccination or receive a vaccine having a lower potency.
Exemplary Privacy Settings
In
some embodiments, the system comprises one or more layers of protection
and/or privacy. In some embodiments, layers of protection include one
or more of encryption algorithms and/or software.
For
example, encryption algorithms and/or software convert the data into
ciphertext to transform the original data to a non-readable format
accessible only to authorized parties who can decrypt the data back to a
readable format. The process of encrypting and decrypting messages
optionally involves keys. The two main types of keys in cryptographic
systems are symmetric-key and public-key (also known as asymmetric-key).
Exemplary
types of keys: Symmetric-keys: In symmetric-key schemes, the encryption
and decryption keys are the same. Communicating parties must have the
same key in order to achieve secure communication. Public Keys: In
public-key encryption schemes, the encryption key is published for
anyone to use and encrypt messages. However, only the receiving party
has access to the decryption key that enables messages to be read. In
some embodiments, the length of the encryption key used in the system is
one or more of 128-bits, 256-bits, 1024-bits and 2048-bits.
In
some embodiments, the privacy of the users that information is being
collected is protected by anonymizing the user at the source. For
example, when a cellular phone/electronic device is used to collect the
relevant data, the name of the owner of the electronic device is either
encrypted and/or anonymized so any interaction with external sources
(for example the servers of the systems) will be managed without the use
of the actual name of the user but using an encrypted and/or anonymized
user name. In a practical example, electronic devices/cellphones are
used to evaluate, quantify and qualify the interactions of the user with
other people during the day. In some embodiments, the cellphone
communicates with other cellphones to monitor the interactions
(distance, location, duration, etc.). In some embodiments, when
collecting the data about the interactions, the software in the
electronic device will use encrypted and/or anonymized user names. For
example, using the names as mentioned in the example below, John Doe,
Jane smith and Mark Lite are three users, all having cellphones and
optionally comprising a dedicated app for this purpose. In some
embodiments, the software of the app in the electronic device will
encrypt and/or anonymize the names to be, for example, John Doe=user
265498756124565526, Jane smith=user 31678465923128 and Mark Lite=user
463212887036554. From this point on, all communications between their
electronic devices and external sources will be performed using the
encrypted and/or anonymized user names. Optionally, for example as
described below, the user IDs or what is exchanged between telephones)
are non-unique. For example, provided at a ratio of, for example 1:100,
1:1000, 1:10000, 1:100000 between codes and individuals. While this may
mean a potential for confusion between individuals, such confusion may
be small, while the increase in difficulty of identifying a use based on
the tracked information can significantly increase.
Furthermore,
when assessing the order of receiving treatment, either individually or
by groups, (e.g., at a server) may comprise the parameters needed to
enter a certain group (for example, the first group to receive
treatment, the second group to receive treatment, etc.). In some
embodiments, the action of comparing between the parameters of each
group and the collected data from the user will be performed inside and
by the electronic device itself, thereby avoiding sending data to the
servers. In some embodiments, the electronic device will contact the
server to requests the parameters, the electronic device will perform
the necessary calculations and will generate a score that will be sent
back to the server in an anonymized matter (as explained before). In
some embodiments, additional information regarding each individual user,
as disclosed above, is also downloaded to the electronic device for use
of calculations. Once the calculations are finished, the resulting data
will be sent to the servers and, in response, the server will
optionally send a notification to the user to go and receive treatment.
It
is a particular feature of some embodiments of the invention that
information about a person's activities, locations, meetings, are not
sent out of the device except as, for example, an overall score or a
priority for treatment. In some cases, the behavior is sent out but is
anonymized and/or condensed, for example, indicating a number (e.g.,
optionally not an exact number and/or time and/or date) of people met
and a number of large congregations attended (optionally not an exact
number and/or location), but with enough details removed so that
identification of an identity of the device owner will be difficult or
impossible.
In
some embodiments, whether the calculations are performed on the servers
or on the electronic device, the encryption and/or anonymizing of the
name of the user is always used. In some embodiments, the means to read
between the encrypted/anonymized user name and the actual name will only
be available in the user's electronic device.
In
some embodiments, the notification for getting treatment may or may not
contain information regarding the results of the calculations. For
example, an individual that was identified as a superspreader may or may
not receive information about the fact that he/she was identified as
such. In some embodiments, the potential advantage of not providing such
information is to further enhance the privacy protection of the user.
For example, an onlooker may not be able to tell if a user received a
high score due to his own behavior, the behavior of those he meets
and/or an underlying health condition, which may put them at higher
risk.
In
some embodiments, dedicated codes, for example in the form of coupons,
will be provided to individuals having important/relevant professions
(like doctors, police, etc.). In some embodiments, insertion of the
codes into their personal electronic devices will inform the system that
that encrypted/anonymized user needs a correction in their score. In
some embodiments, the correction can be either increasing the score or
decreasing the score. In some embodiments, when the electronic device
detects certain behavior, like an increase in the movements of the user,
the electronic device (for example via the dedicated app) will warn the
user that his score will be changed if the behavior is not changed. In
some embodiments, changing the score can be either increasing or
decreasing the score.
Exemplary Methods for Identifying Superspreaders with High Levels of Anonymization
It
has been shown that individuals are concerned that the authorities
and/or companies are constantly collecting data with or without their
consent for a plurality of reasons. It is also scope of some embodiments
of the invention to provide a method of identifying superspreaders
without the need to collect data that could potentially be used to lead
to the identification of the person in question.
As
an example, consider three types of systems having different levels of
possible anonymization techniques, in accordance with various exemplary
embodiments of the invention:
1. A system that uses personal information but does not transmits that personal information about the individual;
2.
A system that uses personal information but does not transmits specific
information that could be used to potentially identify the individual;
and
3. A system that does not require any personal information to work.
In
some embodiments, the anonymization techniques described in the
“Exemplary Privacy Settings” section belong to the first type and/or the
second type of technique, where relevant data (positional data,
personal data, etc.) is used by the system but: a) anything that is
transmitted is either coded and/or anonymized when used, or b) the
necessary calculations are performed on the electronic device itself,
thereby avoiding sending any personal data at all.
In
the following paragraphs, systems belonging to the third type of system
comprising a method of identifying a superspreader that potentially
does not require the use of any personal information will be explained.
Exemplary “ID” Based System for the Identification of Superspreaders
In
some embodiments, the system is based on the following assumptions: 1)
all individuals comprise an electronic device of any kind; 2) on each
electronic device there is installed a dedicated application/app that
runs the system's software (as will be explained in the following
paragraphs); and 3) when individuals meet other individuals, information
is passed between their electronic devices.
Referring to FIGS. 5a-f ,
showing flowcharts of exemplary methods of identification of
superspreaders, with an anonymization, according to some embodiments of
the invention. In some embodiments, the method begins when a user
downloads the software, in the form of an application (or app) into
their electronic device 502.
In some embodiments, dedicated electronic devices comprising the
software will be distributed to those individuals who either do not
possess an electronic device or do not want the software downloaded into
their electronic devices. In some cases, the device has such software
preinstalled thereon.
In
some embodiments, when the individual opens the application,
optionally, the individual will be requested to provide and/or insert an
identification (ID), optionally using alphanumeric digits 504,
optionally comprising a high number of digits, for example 10 digits,
20 digits, 40 digits. In some embodiments, the system will automatically
provide an ID to the device (e.g., will be generated locally, for
example, as a random number or as an encrypted version of the user ID.
To facilitate the explanations below, a 20 digits ID will be assumed. It
should be understood that other length of ID can be used, noting the
difference between IDs that are expected unique and IDs that are not
expected to be unique and within unique IDs, IDs that also a particular
part thereof is long enough to be expected to be unique.
At
this point, all users have an electronic device with a software in the
form and/or as part of an application in which an ID comprising 20
digits has been assigned to the device. It should be noted that the use
of “application”, “app” and “software” are interchangeable for the
explanation of the following methods. From here, four different methods
can be used, as will be further explained bellow.
Anonymized Method 1—Count
Referring to FIG. 5b ,
showing a flowchart of exemplary anonymized method 1, according to some
embodiments of the invention. Following the letter “A” from FIG. 5a to FIG. 5b , in some embodiments, when an electronic device comes in proximity to another electronic device, the devices exchange full IDs 506
between each other, and the software saves the received ID in the
application itself. In some embodiments, after a certain period of time,
for example, after one day, after 7 days, after 14 days, after 30 days,
or intermediate or shorter times and/or on request by a central server,
the application analyzes the IDs stored in the electronic device 508.
In some embodiments, analyzing comprises one or more of counting the
number of IDs that were received, the number of times that a specific ID
was received and the number of IDs received in a day. In some
embodiments of the invention, the counting is weighted so different IDs
get a different weight, for example, IDs with a high score may be
weighted higher, for example as described herein. In particular, IDs
that are associated with contacting other suspected superspreaders may
receive a higher score. In some embodiments, the software then generates
a score based on the result of the analysis.
At this point one of two different methods is optionally applied, a completely anonymous method and a semi-anonymous method 510.
In some embodiments, when the method is a completely anonymous method, the method continues following the letter “E” back to FIG. 5 a.
In some embodiments, the application receives from the server a scale of scores 512.
For example, continuing using the scale as above, from 1 to 100, group 1
are those individuals having a score higher than 90, group 2 are those
individuals having a score from 80 to 90, and so on. In some
embodiments, the software then compares the score generated from the
analysis with the scale of scores 514.
In some embodiments, based on the result of the comparison, the
software provides the user of the device with relevant information
related the treatment to be received. For example, a predetermined date
to receive vaccination (information received with the scale of scores
from the server) and/or the group number for receiving the vaccination.
In some embodiments of the invention, the scale of scores is generated
by the receiving information about the score distribution and selecting
cutoff values optionally based on available vaccines. Optionally, the
information comprises receiving scores form some or all devices.
Optionally, only a statistical same of scores is used, for example,
fewer than 10%, 1%, 0.1% of available devices, for example, between 50
and 10,000 scores. It is noted that such scores may be delivered
anonymously, for example, using an anonymous web service, optionally
anonymized using anonymity tools such as Tor, so that the deliverer of
each score is unknown. Optionally, the scores are digitally signed by
the sender.
Returning to FIG. 5b , in some embodiments, when the method is not a completely anonymous method, the method continues following the letter “F” to FIG. 5 f.
In
some embodiments, after the software has generated a score based on the
analysis, the software sends the score, together with the full ID (here
and in other examples, a full ID may be encrypted or Hashed or
otherwise used to generate a token, which, optionally, is not
decipherable by the server), to the server to be used to evaluate if
that specific individual is potentially a superspreader or not, when
compared to other users 518. In some embodiments, the server performs an evaluation by comparing the scores of the different IDs 520
and generates a treatment list according to the result of the
evaluation. In some embodiments, the server then sends back notification
regarding the vaccination procedures 522, for example, when to go to receive a vaccination, the group number, etc.
In
some embodiments, optionally, the user can choose to respond to a
series of personal questions presented by the application, which are
then translated into factors that affect the score, for example as
disclosed herein.
In
some embodiments, the user choses the level of anonymity that the
system will work (completely anonymous or partially anonymous), e.g.,
different individuals may have different anonymity levels in a same
vaccination prioritization system.
Anonymized Method 2—Count with Transmission of Partial Username
Referring to FIG. 5c ,
showing a flowchart of exemplary anonymized method 2, according to some
embodiments of the invention. Following the letter “B” from FIG. 5a to FIG. 5c ,
in some embodiments, when an electronic device comes in proximity to
another electronic device, the devices exchange partial IDs, for example
only the last 10 digits of the 20 digits of the ID 524,
and the application saves the received partial ID in the application
itself. In some embodiments, the partial ID is a substantially unique
partial ID. For example, the use of the last 10 digits of the 20 digits
increases the chances that the partial ID is a substantially unique
partial ID. In some embodiments, the partial ID is a substantially
non-unique partial ID. For example, the use of the last 3 digits of the
20 digits increases the chances that the partial ID is a substantially
non-unique partial ID, since there is an increased chance that the same
last 3 digits appear in more than one ID. It should be understood that
the word “substantially” in this context does not mean to be vague, but
it is related to the statistical probabilities that a presented partial
ID could be identical to another.
In
some embodiments, a potential advantage of exchanging only partial IDs
is that it decreases the chances that the specific individual could be
identified. It is also noted that, in some embodiments, transmitting
partial ID might introduce errors to the analysis of the meeting between
individuals since it increases the possibility that one or more
individuals will transmit the same partial ID. Since the scope of the
method is to protect the privacy of the individuals while contemporarily
providing an indication of a potential superspreader, a certain margin
of error is acceptable.
In
some embodiments, when a received partial ID is stored in the
application, it is stored (or only transmitted that way) by adding its
own partial ID. In some embodiments, a potential advantage of using this
method is that if such pairs of partial ids are transmitted to a third
party, such third party can track and count unique meetings.
In
some embodiments, after a certain period of time, for example, after 7
days, after 14 days, after 30 days (or other times for as discussed in
the previous method), the application analyzes the partial IDs stored in
the electronic device 524.
In some embodiments, analyzing comprises one or more of counting the
number of partial IDs that were received, the number of times that a
specific partial ID was received and the number of partial IDs received
in a day. In some embodiments, the software then generates a score based
on the result of the analysis. In some embodiments of the invention, a
repeat meeting with a same partial ID is not counted or given a lower
weight. Other methods of counting as described herein may be used. In
some embodiments of the invention, the count is otherwise normalized.
For example, the distribution of counts may be used to reconstruct an
estimate of actual diversity of meetings, using statistical methods of
distribution estimation, such as known in the art. Such methods may also
be used if instead of always transmitting the ID the ID is only
sometimes transmitted. This statistical distribution may be used to
estimate the percentage of unique meetings vs percentage of repeat
meetings, for example, assuming a given distribution shape for repeat
meetings. Such a given shape may be provided, for example, by a central
server (e.g., based on real-time data collection) or a priori.
Optionally or additionally, such distribution may be created by
sometimes applying method 1 of full ID transmission.
At this point, one of two different methods is optionally applied, a completely anonymous method and a semi-anonymous method 528. In some embodiments, when the method is a completely anonymous method, the method continues following the letter “E” back to FIG. 5 a.
In some embodiments, when the method is not a completely anonymous method, the method continues following the letter “F” to FIG. 5f . These alternatives may be applied as above.
Anonymized Method 3—Count with Transmission of Partial Username and Username Changes Periodically
In this method, which can be used as a variant of the last two methods, and is shown in FIG. 5d , the ID or partial ID used by the device is modified.
In
some embodiments, for example, after the certain period of time
mentioned above for counting, the partial ID that is used for the
transmission of IDs between is changed by the system and/or the
individual itself 534. The actual
ID may be changed or a different part of the ID transmitted. In some
embodiments of the invention, the original ID is used as a seed to
generate a series of pseudo random IDs to be used for transmission. In
some embodiments, for example, when the system changes the transmitted
partial ID, the system transmits instead of the last 10 digits of the
ID, the first 10 digits of the ID; or for example the first 5 digits
together with the last 5 digits. It should be understood that the
above-mentioned are only examples, and that other methods of randomizing
the partial ID that is transmitted are also included in the scope of
some embodiments of the invention. In some embodiments, periodically
changing the partial ID may further cause to errors since it further
increases the possibility that one or more individuals will transmit the
same partial ID. As mentioned above, a further certain margin of error
is still acceptable.
The method then continues with various options for acting on the score, for example, a completely anonymous method and a semi-anonymous method 536. In some embodiments, when the method is a completely anonymous method, the method continues following the letter “E” back to FIG. 5 a.
In some embodiments, when the method is not a completely anonymous method, the method continues following the letter “F” to FIG. 5 f.
In
this and other embodiments it is noted that other follow up activities
may be provided in addition or instead, in particular, activity by a
central server may be reduced. For example, a user may simply go to a
vaccinating station and show their score and be given a vaccination or
date therefore accordingly.
Anonymized Method 4—Complex Count with Transmission of Partial Username, at Least One Additional Number and Optionally Username Changes Periodically
Anonymized Method 4—Complex Count with Transmission of Partial Username, at Least One Additional Number and Optionally Username Changes Periodically
Referring to FIG. 5e ,
showing a flowchart of exemplary anonymized method 4, according to some
embodiments of the invention. In some embodiments, a complex count
method is used for probabilistically determining if a certain individual
is a potential superspreader. In some embodiments, the complex count
method comprises the use of two independent counts for the
determination.
Following the letter “D” from FIG. 5a to FIG. 5e ,
in some embodiments, when an electronic device is in proximity to
another electronic device, the system is configured to exchange not one,
but at least two ID numbers as following.
In some embodiments, the first number to be exchanged is the partial ID 538.
In some embodiments, the exchange of the first number is as disclosed
in method 1, where the full ID is exchanged. In some embodiments, the
exchange of the first number is as disclosed in either method 2 or
method 3, where a partial ID is exchanged. For the explanation of the
method and as disclosed in FIG. 5e ,
the explanation will refer to the transmission of a partial ID. It
should be understood that this method could also be applied when
transmitting the full ID.
In some embodiments, the first number is used to evaluate the number of contacts.
In
some embodiments, the second number to be exchanged is a different set
of digits, either created by the system or inserted by the user itself 538.
In some embodiments, the actual second number to be exchanged is a
partial second number, similar to what is done with the first number.
In
some embodiments, the second number is used to evaluate if the
individual is meeting people from outside a limited subpopulation and/or
track the general promiscuousness (optionally in a non-sexual sense) of
such individuals.
In
some embodiments, contrary to the first number that always is exchanged
in an encounter, the second number is exchanged at a certain “rate of
probability”. In some embodiments, a rate of probability is, for
example, a calculated number that responds to the question: what is the
percentage rate necessary to separate between a superspreader and a
non-superspreader. In some embodiments, the rate of probability is
achieved by running a simulation, and checking for different probability
rates the degree of discrimination. For example, a rate of probability
can be 3%, 5%, 10%, 20% or smaller or intermediate values. In some
embodiments, this means that, if the rate of probability is 3% for
example, an electronic device that encounters 100 electronic devices
will exchange 100 times (100% of the times) the first number and 3 times
(3% of the times), in addition to the first number, will also exchange
the second number. In some embodiments, the rate of probability is lower
than 100.
In
some embodiments, from the moment the system is activated, the
electronic devices of the individuals will begin collecting first and
second numbers as long as they continue to meet other electronic
devices.
In
some embodiments, when a certain electronic device exchanges the second
number (under the rate of probability), the electronic device will
exchange in addition to its second number, all second numbers that were
collected until that moment. In some embodiments, potentially and
probabilistically, an individual that is a superspreader will collect a
high number of second numbers because he/she meets many different
individuals, who themselves meet different individuals. While an
individual “trapped” in a subpopulation may only collect at most as many
numbers are there are persons in the subpopulation. Therefore, in some
embodiments, when someone meets that superspreader, many second numbers
will be potentially exchanged from that superspreader to that someone.
In some embodiments, those second numbers collected from other
individuals will later be used to indicate a specific meeting between an
individual and a superspreader.
In
some embodiments, an individual that collects many second numbers,
potentially and probabilistically, met a superspreader and/or is one
themselves. In some embodiments, this information is used to cause an
effect (e.g., increase) in the scoring of the individual and/or in the
weight of the contact.
The collected IDs may be counted after a time, e.g., as described in the other methods (540)
In some embodiments, optionally, after the certain period of time
mentioned above, the partial ID transmitted between devices is changed
by the system and/or the individual itself 542 as disclosed above.
Optionally, a method of follow-up is selected, for example, a completely anonymous method and a semi-anonymous method 544. In some embodiments, when the method is a completely anonymous method, the method continues following the letter “E” back to FIG. 5 a.
In some embodiments, when the method is not a completely anonymous method, the method continues following the letter “F” to FIG. 5f , for example as described above.
In
any of the above methods, optionally, statistical information about
collected first and/or second numbers (e.g., how many people had how
many collected first and/or second numbers) may be transmitted to the
server to help generate a better picture of these statistics of the
population's collected information.
In
some embodiments of the invention, more than one second number is used.
Optionally, each additional such number is transmitted at a different
probability. This allows different numbers to give information about
different characteristics of subpopulations. It is noted that if only
one number is used and its transmission rate not selected correctly, it
may result is propagation of such second number over a significant part
of the network of contacts, making it less useful for identifying more
closed and more open parts of the network.
In
some embodiments of the invention, no additional second number is used.
Rather the first number is optionally counted and/or transmitted using
such probabilistic transmission rate. So, for example, during a contact,
the second device will store the received ID of the first contact in a
memory for storing and/or counting contacts with a first ID and also,
with some probability store that number in a second memory used for
counting and/or tracking second numbers. Additional memories may be
provided if more numbers are tracked.
In
some embodiments of the invention, a relatively small non-unique ID is
used and this ID may be used as an index for the first and/or second
memory. For example, when meeting an individual who passes a non-unique
ID 234, memory location 234
is increment (optionally in a weighted manner). If a second ID list,
say (123, 456, 789) is passed, the count in each of those indexes in the
second memory is incremented (optionally in a weighted manner). In some
embodiments, only one bit (or an equivalent thereof) is saved for each
ID in the second memory and it is either set or unset. Optionally, the
second ID uses more bits than the first ID, for example, 2, 3, 4, 5
times as many bits or an intermediate of smaller or greater number. This
may allow preventing saturation of second ID tracking. Optionally or
additionally, a statistical estimation of the actual number of second
IDs is reconstructed using statistical methods and the number of second
IDs received and optionally a count of at least a sample thereof.
Optionally, an assumption is made about the expected shape of
distribution of second IDs.
Optionally
or additionally, the number of second IDs collected is tracked as a
function of time. Optionally, potential superspreaders (and which get an
increased score and/or contact weight) are those who early on
accumulate a larger number of second IDs (e.g., as compared to other
persons an individual comes in contact with) and/or those persons (e.g.,
with repeated contact) whose second ID count asymptotes later or not at
all.
Regarding
repeat meetings with an individual, it is noted that an individual is a
sum of all his contacts, so that after a time, if and as that
individual meets new contacts, the individual changes and should be
weighted more heavily. Such tracking can be by time and/or can be by
change in count of first and/or second IDs that an individual has, which
count (and/or a date of contact) is optionally transmitted upon meeting
and may be stored.
Exemplary Effect of Meeting an Individual that has Met Potential Superspreaders
Referring now to FIG. 6,
showing a schematic flowchart of an example of the effect caused when a
certain individual meets another individual that had been in contact
with possible superspreaders, according to some embodiments of the
invention. In some embodiments, as previously mentioned, when a Device A
meets Device B 602, IDs are exchanged and optionally also information regarding previous meetings 604.
In some embodiments, for example, the software in Device A, that has
just received the ID and previous meetings of Device B, will evaluate
the received data 606. In some embodiments, evaluation of data comprises one or more of evaluating the number of meetings Device B has had 608 and the kind of individuals were met during those meetings 610.
In some embodiments, since these operations were also previously
performed by Device B during its meetings, the information about the
possible meeting with a potential superspreader will be also delivered
by Device B to Device A, when information is exchanged. In some
embodiments, the software in Device A will generate a score to the
meeting between Device A and Device B, also in view of the information
regarding the kind of individuals that Device B has met 612. In some embodiments, the score is then saved in Device A 614 to be used in the final score calculations, as previously described.
Exemplary Methods
In some embodiments, an exemplary method of providing the order of treatments to a population comprises:
1. Collecting relevant data regarding each individual in the population, according to predetermined parameters.
2. Providing a superspreading score to each of the individuals according to a formula using the predetermined parameters.
3. Ordering each individual according to his or her superspreading score from high to low.
4. Optionally dividing all individuals in groups according to their superspreading score.
In some embodiments, after the list is ready, optionally in groups:
5. Notifying the individuals with a location and a time to receive the treatments.
6.
Treating the population according to their superspreading score,
optionally by groups, where individuals and/or groups hiving the higher
scores will receive first the treatments. In some embodiments of the
invention, treatment is rather testing, as testing superspreaders may be
a faster and more effective way of detecting a resurging pandemic.
Exemplary System
Exemplary System
In
some embodiments, the system comprises a computer network architecture
optionally with machine learning and/or other artificial intelligence
tools to allow for the automated prioritization of treatments in a
pandemic event. In some embodiments, the system allows for
prioritization of treatments using information regarding subjects in a
population, disease process and progression, number of available
treatment doses, and a plurality physical location attributes. In some
embodiments, this potentially enables relevant authorities to measure,
predict and/or improve their health-related performance during a
pandemic. In some embodiments, this in turn enables relevant
decision-making personnel and healthcare providers to get a true
quantitative sense of what is possible to achieve with any given
population of patients, in view of the parameters that define each
individual and the population.
The following is an example of the workflow of a user experience with a system of the present invention:
1. A user makes a request for an analysis and list generation report to the system.
2.
The system uses an analytics module (A.M.) to analyze the information
of the population (for example, information as disclosed above).
3.
The system automatically issues a request to a Database Module (DB.M)
to provide all relevant information and/or issues a request to external
sources (see above) to provide the required information and/or issues a
request to a simulations module (S.M) to perform the necessary
simulations.
4. The analytics module (A.M.) collates the results into a unified analysis response, based on any combination of the subjects in the population and factors and/or components data available. In some embodiments of the invention, the A.M includes a ML module (optionally in the form of an analytic system or a neural network) which is used to predict transmission and super-spreader potential of an individual based on their past behavior. Optionally, an initial model is provided for such mapping. Optionally, the ML module also receives actual infection information, for example, by automated collection from medical records or from epidemiological studies (e.g., of some or all infected people) and uses this information to update the model, for example, using a machine learning method as known in the art, to generate a prediction of infectiveness (and/or superspreader potential) of an individual given his contacts and the superspreader potential of similar individuals. In some embodiments of the invention, statistical methods are used instead of or in addition to ML methods. Optionally or additionally, what is created is a classifier, which classifies an individual as a potential superspreader. Such a classifier can build a classification scheme given a set of individuals, each with behaviors and actual infectiveness as determined, for example, using epidemiological studies and/or contact tracking combined with disease detection in such tracked contacts. Such classifier may be used (or transmitted to individual devices to be used instead of and/or in addition to counting for example as described herein) to generate a general score for an individual based on the classification and optionally based on additional information, such as medical risk.
4. The analytics module (A.M.) collates the results into a unified analysis response, based on any combination of the subjects in the population and factors and/or components data available. In some embodiments of the invention, the A.M includes a ML module (optionally in the form of an analytic system or a neural network) which is used to predict transmission and super-spreader potential of an individual based on their past behavior. Optionally, an initial model is provided for such mapping. Optionally, the ML module also receives actual infection information, for example, by automated collection from medical records or from epidemiological studies (e.g., of some or all infected people) and uses this information to update the model, for example, using a machine learning method as known in the art, to generate a prediction of infectiveness (and/or superspreader potential) of an individual given his contacts and the superspreader potential of similar individuals. In some embodiments of the invention, statistical methods are used instead of or in addition to ML methods. Optionally or additionally, what is created is a classifier, which classifies an individual as a potential superspreader. Such a classifier can build a classification scheme given a set of individuals, each with behaviors and actual infectiveness as determined, for example, using epidemiological studies and/or contact tracking combined with disease detection in such tracked contacts. Such classifier may be used (or transmitted to individual devices to be used instead of and/or in addition to counting for example as described herein) to generate a general score for an individual based on the classification and optionally based on additional information, such as medical risk.
Optionally
or additionally, the AM includes one or more optimization tools which
given the various inputs described herein and/or one or more goals,
optimizes vaccine delivery and/or schedule to achieve a better approach
to the goal.
5.
The analytics module (A.M.) serves the response back to the system, and
transmits the list to the user, and the list is now available to the
relevant personnel. In some embodiments, this potentially helps the
relevant personnel to decide whom, when and where distribute the
available doses to the population.
Each
and any of such modules may be implemented, for example, using a
central server, a distributed server and/or a cloud implementation.
In
some embodiments, the system may automatically use the simulation
models to select and apply a predictive model for the preferred
deployment of the doses (for example, the parameter may be number of
available doses or the higher number of individuals protected by the act
of vaccination and/or a total number of expected of deaths and/or time
to reach a threshold where one or more limitations on society may be
removed). In some embodiments, the system may then predict the
performance of an underperforming vaccination result (if no changes are
made to trend performance) and predict the performance of the same
treatment result if the requirements are met, and then compare the
before and after predicted performance to show the impact of meeting the
requirements. A report of the requirements and of the predicted impacts
of meeting the requirements may then be prepared by the system, and
transmitted to the user.
FIG. 7
schematically illustrates components of an exemplary system comprising a
computer network architecture usable in some embodiments of the
invention, comprising at least one optional server 702, an optional analytics module (A.M.) 704, an optional Database Module (DB.M) 706, and/or optional access to various third-party databases and sources 708, and an optional simulations module 712.
In some embodiments, a user using a user device 710 accesses the at least one server 702. In some embodiments, the user transmits a user request to the analytics module (A.M.) 704 for analysis of data and the generation of a list 716. In some embodiments, analytics module (A.M.) 704 accesses the Database Module (DB.M) 706 either directly and/or via the server 702. In some embodiments, the analytics module (A.M.) 704 accesses through various identified third party and sources 708. In some embodiments, data accessed from third-party databases and sources 708 may be analyzed and stored in Database Module (DB.M) 706, thus supporting the simulations module 712, which performs machine-learning prediction activities. In some embodiments, the analytics module (A.M.) 704 may also access data received from the simulations module 712 and previously stored in the Database Module (DB.M) 706, thus benefiting from the machine learning and artificial intelligence of the simulations module 712.
In some embodiments, the system optionally comprises a prediction module 714 with a prediction generator and in communication with the simulation module 712 and with the database module 706.
Not
shown is a vaccination management server, which is optionally a
separate component of the system or be a separate system. In some
embodiments of the invention, this server is used to manage distribution
of vaccinations (e.g., locations and/or times) and/or tracking of
subjects that requested vaccination and/or received such vaccination.
Optionally, this server manages the logistics of vaccine distribution
using the information form the system indicating which subjects are to
be vaccinated and in what order. In some embodiments of the invention,
vaccinations are distributed based on population density and the
vaccination management server is used to track subjects receiving
vaccinations to ensure that they are not vaccinated out of turn, for
example, by comparing prioritization data provided by the devices
against a record of prioritization intentions.
In
some embodiments, the system allows automatic machine learning as new
data sources are added, and new data is collected, and the predictive
algorithms are recalibrated and reselected using the expanded, and hence
more reliable, data. In some embodiments, this may potentially enable
users of the system to quickly realize the value of new data.
In
some embodiments, the system utilizes machine learning, optionally
incorporated in predictive model algorithms to execute predictive
analytical operations. Learning may be supervised or unsupervised. In
general, a predictive model analyzes historical data to identify
patterns in the data. The patterns identified may include relationships
between various events, characteristics, or other attributes of the data
being analyzed. Modeling of such patterns may provide a predictive
model whereby predictions may be made. Development of predictive models
may employ mathematical or statistical modeling techniques such as curve
fitting, smoothing, and regression analysis to fit or train the data.
Such techniques may be used to model the distribution and relationships
of the variables, e.g., how one or more events, characteristics, or
circumstances (which may be referred to as “independent variables” or
“predictor variables”) relate to an event or outcome (which may be
referred to as a “dependent variable” or “response”).
In
some embodiments, a machine learning process may include developing a
predictive model. For example, a dataset comprising observed data may be
input into a modeling process for mapping of the variables within the
data. The mapped data may be used to develop a predictive model. The
machine learning process may also include utilizing the predictive model
to make predictions regarding a specified outcome that is a dependent
variable with respect to the predictive model. The machine may then be
provided an input of one or more observed predictor variables upon which
the output or response is requested. By executing the machine-learning
algorithm utilizing the input, the requested response may be generated
and outputted. Thus, based on the presence or occurrence of a known
predictor variable, the machine-learning algorithm may be used to
predict a related future event or the probability of the future event.
It
is noted that a most basic prediction may be used, e.g., behavior in
past predicts behavior in future. For example, if a person regularly
meets 30 people a day for over 15 minutes each and within 2 meters and I
a location that is closed (e.g., based on mapping data sources), it is
assumed that may continue. Similarly, if a person attends a church of
200 people once a week, that may be assumed to continue. In addition,
class behavior may be applied. For example, if the person is collage
age, the system may be programmed with an expectation of a certain
number and/or expected dates and/or expected probability of parties such
a person might attend. Such information may also be generate by
statistically analyzing the behavior of others in that person' cohort.
In some embodiments, once the treatment order list 716 is ready, individual messages 718 are sent to the specific individuals notifying them where and when they should go to be treated.
The
architecture of the system may depend on the implementation. For
example, if the system is mainly anonymous, with scorings being
generated on individual cellphones (or other devices), the server may be
used to generate information to be used by the cellphones and/or to
collate results generate vaccination prioritization plans and/or invite
individuals to be vaccinated.
In
such an example, the software of the electronic device may increase in
relative importance. Such device may include a memory (e.g., as noted
herein) for storing actual IDs or partial IDs and/or counts thereof.
Optionally or additionally, such device includes an ID generator.
Optionally or additionally, such device includes communication software
(e.g., addresses) for making an anonymous drop of information and/or for
receiving a general broadcast of information (e.g., from the server)
and/or for accessing an individual's EMR or other repository with
relevant medical information. Optionally or additionally, such a device
includes a count analysis and/or other module that applies a
classification or scoring method for example, as described herein.
Optionally or additionally, such a device includes a sensor an
associated software for detecting infection related information, for
example, being indoors, location, distance from other electronic
devices, duration at such distance, coughing sounds and/or video or
still analysis to detect mask wearing. Optionally or additionally, such a
device includes a display and associated software for showing a
vaccination invitation and/or a score. Optionally or additionally, such a
device includes an input (e.g., a camera) for receiving information
form printed or other screens, for example, a user's occupation or
special dispensation. Optionally or additionally, such device includes
software, which generates behavior alerts to the user, for example, when
the user engages in riskier behavior.
Various
embodiments and aspects of the present invention as delineated
hereinabove and as claimed in the claims section below find calculated
support in the following examples.
EXAMPLE
Reference
is now made to the following prophetic examples, which together with
the above descriptions illustrate some embodiments of the invention in a
non limiting fashion.
In
the following example, three imaginary individuals (John Doe, Jane
Smith and Mark Lite) will be scored according to one or more exemplary
factors and/or components, as disclosed above. It should be understood
that the following scenario is not limiting and it is only provided to
enable a person having skills in the art to implement the invention.
Background Information
| | | |||
| | John Doe | Jane Smith | Mark Lite | |
| | | |||
| | ||||
| Age (relative weight 1%) | 30 | 35 | 33 |
| Profession (relative | Teacher | Operator | Unemployed |
| weight 5%) | |||
| Known health conditions | None | Chronic coughing | None |
| (relative weight 4%) | |||
| Visits religious gathering | No | Yes | Yes |
| (relative weight 20%) | |||
| | |||
Weekly Mobility Data
| |
|
|||
| |
John Doe | Jane Smith | Mark Lite | |
| |
|
|||
| |
||||
| Day 1 | Total locations visited: 5 | Total locations visited: 3 | Total locations visited: 1 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 650 | on this day: 150 | on this day: 5 |
| Day 2 | Total locations visited: 6 | Total locations visited: 4 | Total locations visited: 1 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 750 | on this day: 250 | on this day: 5 |
| Day 3 | Total locations visited: 5 | Total locations visited: 2 | Total locations visited: 2 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 650 | on this day: 80 | on this day: 30 |
| Day 4 | Total locations visited: 5 | Total locations visited: 2 | Total locations visited: 1 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 650 | on this day: 80 | on this day: 5 |
| Day 5 | Total locations visited: 5 | Total locations visited: 3 | Total locations visited: 2 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 650 | on this day: 150 | on this day: 30 |
| Day 6 | Total locations visited: 5 | Total locations visited: 1 | Total locations visited: 1 |
| |
Estimated potential | Estimated potential | Estimated potential |
| |
number of individuals | number of individuals | number of individuals |
| |
in contact with subject | in contact with subject | in contact with subject |
| |
on this day: 650 | on this day: 5 | on this day: 5 |
| Day 7 | Total locations visited: 5 | Total locations visited: 2 | Total locations visited: 3 |
| |
Estimated potential | (*visited Church) | (*visited stadium) |
| |
number of individuals | Estimated potential | Estimated potential |
| |
in contact with subject | number of individuals | number of individuals |
| |
on this day: 650 | in contact with subject | in contact with subject |
| |
|
on this day: 500 | on this day: 500 |
| Score | 80 | 60 | 15 |
| (relative | |||
| weight 70%) | |||
| |
|||
In
view of the results of the Weekly mobility data alone, the order of the
treatments will be John Doe, Jane Smith and then Mark Lite.
The calculation of the overall score is:
| |
|||
| criteria | John Doe | Jane Smith | Mark Lite |
| |
|||
| |
|||
| Age | 1% | 50 | 50 | 50 |
| Profession | 5% | 80 | 50 | 0 |
| Known health conditions | 4% | 0 | 90 | 0 |
| Visits religious gathering | 20% | 0 | 80 | 80 |
| Mobility data | 70% | 80 | 60 | 15 |
| weighted scores | 100% | 60.5 | 66.2 | 14.2 |
| |
||||
As
can be seen, when taking under consideration all the information data,
the order of the treatments will be Jane Smith, John Doe and then Mark
Lite.
It
should be understood that the above numeric examples are just examples
to help a person having skills in the art to understand the invention.
It also should be understood that different weight values, scores and
methods of calculating a score could be used.
It
is expected that during the life of a patent maturing from this
application many relevant parameters of scoring activity of individuals
and methods of measuring said parameters will be developed; the scope of
the invention herein is intended to include all such new technologies a
priori.
As used herein with reference to quantity or value, the term “about” means “within ±20% of”.
The
terms “comprises”, “comprising”, “includes”, “including”, “has”,
“having” and their conjugates mean “including but not limited to”.
The term “consisting of” means “including and limited to”.
The
term “consisting essentially of” means that the composition, method or
structure may include additional ingredients, steps and/or parts, but
only if the additional ingredients, steps and/or parts do not materially
alter the basic and novel characteristics of the claimed composition,
method or structure.
As
used herein, the singular forms “a”, “an” and “the” include plural
references unless the context clearly dictates otherwise. For example,
the term “a compound” or “at least one compound” may include a plurality
of compounds, including mixtures thereof.
Throughout
this application, embodiments of this invention may be presented with
reference to a range format. It should be understood that the
description in range format is merely for convenience and brevity and
should not be construed as an inflexible limitation on the scope of the
invention. Accordingly, the description of a range should be considered
to have specifically disclosed all the possible subranges as well as
individual numerical values within that range. For example, description
of a range such as “from 1 to 6” should be considered to have
specifically disclosed subranges such as “from 1 to 3”, “from 1 to 4”,
“from 1 to 5”, “from 2 to 4”, “from 2 to 6”, “from 3 to 6”, etc.; as
well as individual numbers within that range, for example, 1, 2, 3, 4,
5, and 6. This applies regardless of the breadth of the range.
Whenever
a numerical range is indicated herein (for example “10-15”, “10 to 15”,
or any pair of numbers linked by these another such range indication),
it is meant to include any number (fractional or integral) within the
indicated range limits, including the range limits, unless the context
clearly dictates otherwise. The phrases “range/ranging/ranges between” a
first indicate number and a second indicate number and
“range/ranging/ranges from” a first indicate number “to”, “up to”,
“until” or “through” (or another such range-indicating term) a second
indicate number are used herein interchangeably and are meant to include
the first and second indicated numbers and all the fractional and
integral numbers therebetween.
Unless
otherwise indicated, numbers used herein and any number ranges based
thereon are approximations within the accuracy of reasonable measurement
and rounding errors as understood by persons skilled in the art.
It
is appreciated that certain features of the invention, which are, for
clarity, described in the context of separate embodiments, may also be
provided in combination in a single embodiment. Conversely, various
features of the invention, which are, for brevity, described in the
context of a single embodiment, may also be provided separately or in
any suitable subcombination or as suitable in any other described
embodiment of the invention. Certain features described in the context
of various embodiments are not to be considered essential features of
those embodiments, unless the embodiment is inoperative without those
elements.
It
is the intent of the applicant(s) that all publications, patents and
patent applications referred to in this specification are to be
incorporated in their entirety by reference into the specification, as
if each individual publication, patent or patent application was
specifically and individually noted when referenced that it is to be
incorporated herein by reference. In addition, citation or
identification of any reference in this application shall not be
construed as an admission that such reference is available as prior art
to the present invention. To the extent that section headings are used,
they should not be construed as necessarily limiting. In addition, any
priority document(s) of this application is/are hereby incorporated
herein by reference in its/their entirety.
Claims (37)
Hide Dependent
Claims (37)
Hide Dependent
What is claimed is:
1. A method of
prophylactically vaccinating a population having a plurality of subjects
with a vaccine against an epidemic infectious disease, said plurality
of subjects each using a smart electronic device, the method comprising:
a. a. using an ID for
each said smart electronic device for determining a propensity of
proximity of each said plurality of subjects; said determining a
propensity of proximity comprises:
i. at a proximity event,
when a particular said smart electronic device of a particular said
subject is in proximity of one or more other of said smart electronic
devices, transmitting an ID or an indication thereof to said one or more
other smart electronic devices and receiving an ID or indication
thereof from said one or more other smart electronic devices, by said
particular smart electronic device;
said proximity event
being an event where said particular said subject could, if infected,
potentially infect other subjects with said infectious disease;
ii. generating a score
reflecting a propensity for proximity, according to a plurality of
received IDs; said propensity of proximity reflecting a chance of
infecting other subjects if said particular said subject becomes
infected;
b. generating for each
said plurality of subjects a prioritization of vaccination based on said
score; said prioritization being higher for subjects having a higher
propensity of proximity; and
c. prophylactically vaccinating particular subjects of said plurality of subjects according to said prioritization.
2. The method according to claim 1, wherein said using an ID comprises using an ID having fewer than 100,000 potential values.
3. The method according to claim 2, wherein said using an ID comprises using a unique ID and also using said ID as a portion of said unique ID.
4. The method according to claim 1, further comprising changing said ID periodically.
5. The method according to claim 1, further comprising using a second ID and transmitting said second ID or indication thereof together with said ID.
6. The method according to claim 5, wherein said using a second ID is carried out only at a fraction of said proximity events.
7. The method according to claim 6, wherein said using comprises using also second IDs previously received from others of said electronic devices.
8. The method according to claim 6,
comprising generating an indication of closeness of a population met by
said electronic device based on said received second IDs.
9. The method according to claim 1, wherein said score depends on an estimation of propensity of proximity of said one or more other devices.
10. The method according to claim 1, wherein said generating said score comprises counting the number of received IDs.
11. The method according to claim 10, wherein said counting comprises counting unique IDs.
12. The method according to claim 10,
wherein said counting comprises counting IDs with a weighted parameter,
said weighted parameter is generated by analyzing said transmitted
second IDs.
13. The method according to claim 1,
wherein said generating for each said plurality of subjects a
prioritization of vaccination comprises transmitting said score to a
server and generating said prioritization on said server.
14. The method according to claim 13, wherein said generating said prioritization comprises comparing scores by different ones of said electronic devices.
15. The method according to claim 1,
wherein said generating for each said plurality of subjects a
prioritization of vaccination comprises generating said prioritization
on said particular electronic device.
16. The method according to claim 15,
wherein said generating said prioritization comprises receiving from a
server a list or a function indication prioritization according to
score.
17. The method according to claim 1,
comprising displaying prophylactically vaccinating instructions on said
particular electronic device based on said generated prioritization.
18. The method of claim 1,
wherein said epidemic infectious disease comprises a corona virus and
wherein said prophylactically vaccinating comprises a vaccination for
said epidemic infectious disease and wherein said prioritization is used
to select subjects at greater risk of transmitting said epidemic
infectious disease during a pandemic to be vaccinated sooner than
subjects less likely to transmit said epidemic infectious disease.
19. The method of claim 1, wherein said ID is an anonymous ID.
20. The method of claim 1,
wherein said (a) and (b) do not comprise providing information
regarding a status related to said infectious disease in said subjects.
21. The method of claim 1, wherein information about said prioritization of vaccination is not transmitted outside said particular electronic device.
22. The method of claim 1,
wherein information about said proximity event is not transmitted
outside said smart electronic device or said one or more other smart
electronic devices.
23. A system for
selecting subjects for prophylactically vaccinating a population having a
plurality of said subjects with a vaccine against an epidemic
infectious disease, comprising:
a. a plurality of smart electronic devices configured to be carried around by said subjects and configured with instructions to:
i. using an ID for each
said smart electronic device for determining a propensity of proximity
of each said plurality of subjects; said determining a propensity of
proximity comprises:
at a proximity event,
when in proximity of another such smart electronic device, transmitting
an ID or an indication thereof to said another smart electronic device
and receive an ID or indication thereof from said another smart
electronic device; said proximity event being an event where a
particular said subject could, if infected, potentially infect other
subjects with said infectious disease;
generating a score
reflecting a propensity for proximity, according to a plurality of such
received IDs; said propensity of proximity reflecting a chance of
infecting other subjects if said particular said subject becomes
infected;
receiving information from a server;
displaying relevant prophylactically vaccinating instructions to said subjects based on said received information;
b. at least one server comprising a memory and a plurality of modules; said memory comprising instructions for:
ii. sending to said
plurality of smart electronic devices information usable by a circuitry
in said plurality of smart electronic devices to display said relevant
prophylactically vaccinating instructions.
24. The system according to claim 23, wherein said information comprises one or more of subject specific information.
25. The system according to claim 23, wherein said information comprises general information usable by a plurality of subjects and devices thereof.
26. The system according to claim 25,
wherein said server is configured with instructions to receive scores
for a plurality of said electronic devices and use said received scores
to generate said general information, said electronic devices configured
to use said general information to determine a relative treatment
priority for their respective subjects.
27. The system according to claim 23, wherein said smart electronic devices comprise a proximity-detecting module using one or more of:
a. physical proximity data received by means of electronic positioning data of said subject;
b. a distance indicating
sensor which indicates physical proximity of the location of a device
in relation to the location of said another device; and
c. historical location data.
28. The system according to claim 23,
wherein said at least one server or said smart electronic devices
comprise instructions to determine a prophylactically vaccination
prioritization based on said propensity for proximity.
29. The system according to claim 27, wherein determine a treatment prioritization further comprises one or more of:
a. generating a score component based on a nature of a location where said physical proximity data is related;
b. generating a score component comprising health data of the subject of one or both smart electronic devices;
c. generating a score component comprising a profession of the subject of one or both smart electronic devices;
d. generating a score
component reflecting relative health risk to said subject if said
subject contracts said epidemic infectious disease; and
e. generating a score component reflecting damage to society if said subject contracts said epidemic infectious disease.
30. The system according to claim 27,
wherein when said physical proximity data is related to a location that
is either indoors or in a closed space, then said score of said subject
of transmitting said epidemic infectious disease increases by a factor
of between about 10 times to about 100 times.
31. The system according to claim 23,
further comprising a prophylactic vaccination server which allocates
prophylactic vaccinations for a corona virus according to said
prophylactically vaccinating instructions.
32. The system according to claim 31, wherein said server comprises a simulation module configured to perform one or both of:
(a) predict the effect of vaccination on disease spread;
(b) predict the effect
of an ID transmission probability on distinguishing between subjects who
contact mainly subjects in a same subpopulation.
33. The system of claim 23,
wherein said smart electronic devices are configured to transmit a
second ID and previously received second IDs, at a probability of less
than 10% and using said received second IDs to generate said score.
34. The system of claim 23, wherein said transmitted ID is a non-unique ID having fewer possible values than 10% of the number of said devices.
35. The system of claim 23, wherein said ID is an anonymous ID.
36. The system of claim 23,
wherein said plurality of smart electronic devices do not comprise
information regarding a status related to said infectious disease in
said subjects.
37. The system according to claim 23,
wherein information about said prophylactically vaccinating
instructions is not transmitted outside a particular smart electronic
device.
Publication number
Priority date
Publication date
Assignee
Title
US20040236604A1
2002-12-20
2004-11-25
Mcnair Douglas S.
System and method for detecting spatiotemporal clusters
US20060036619A1
2004-08-09
2006-02-16
Oren Fuerst
Method
for accessing and analyzing medically related information from multiple
sources collected into one or more databases for deriving illness
probability and/or for generating alerts for the detection of emergency
events relating to disease management including HIV and SARS, and for
syndromic surveillance of infectious disease and for predicting risk of
adverse events to one or more drugs
US20060218010A1
2004-10-18
2006-09-28
Bioveris Corporation
Systems and methods for obtaining, storing, processing and utilizing immunologic information of individuals and populations
US20080091471A1
2005-10-18
2008-04-17
Bioveris Corporation
Systems
and methods for obtaining, storing, processing and utilizing immunologic
and other information of individuals and populations
US20090319295A1
2006-07-25
2009-12-24
Kass-Hout Taha A
Global disease surveillance platform, and corresponding system and method
US7705723B2
2006-03-15
2010-04-27
Dp Technologies, Inc.
Method and apparatus to provide outbreak notifications based on historical location data
US20110238432A1
2010-03-25
2011-09-29
Vaxcare Corporation
Method and system for optimized distribution and administration of vaccinations
US20120274464A1
*
2008-04-30
2012-11-01
Centurylink Intellectual Property Llc
Routing Communications to a Person within a Facility
US8645538B2
2011-11-20
2014-02-04
Yang Pan
System and method for monitoring outbreak of contagious diseases
US9075909B2
2011-11-20
2015-07-07
Flurensics Inc.
System and method to enable detection of viral infection by users of electronic communication devices
US20150350850A1
*
2014-05-27
2015-12-03
Qualcomm Incorporated
Location support using a device identification conveyed by a positioning protocol
US20200279464A1
*
2017-05-17
2020-09-03
Blue Storm Media, Inc.
Certification and distancing display on a user-worn digital badge device for infection prevention
US20200388382A1
*
2014-09-22
2020-12-10
Peter Costantino
Systems for identification and tracking vital signs in the field with redundant electronic/visual security mechanisms
US20210020294A1
*
2019-07-18
2021-01-21
Pacesetter, Inc.
Methods, devices and systems for holistic integrated healthcare patient management
Family To Family Citations
US10198779B2
*
2016-06-03
2019-02-05
Blyncsy, Inc.
Tracking proximity relationships and uses thereof
US11367535B2
*
2018-09-30
2022-06-21
Hill-Rom Services, Inc.
Patient care system for a home environment
US20200242566A1
*
2019-01-30
2020-07-30
Pacesetter, Inc.
System and method of prioritizing patients for clinician management
US11170898B2
*
2019-09-30
2021-11-09
Kpn Innovations, Llc
Methods and systems for prioritizing user symptom complaint inputs
US11107588B2
2020-08-11
2021-08-31
Gal EHRLICH
Methods
and systems of prioritizing treatments, vaccination, testing and/or
activities while protecting the privacy of individuals
* Cited by examiner, † Cited by third party
Non-Patent Citations (89)
Title
Abbasi et al. "Modeling Vaccine Allocations in the
COVID-19 Pandemic: A Case Study in Australia", Available at SSRN, p.
1-34, Dec. 9, 2020.
ACS "2011-2015 5-Year ACS Commuting Flows: Tables", ACS, 1 P., Last Revised Oct. 24, 2019.
Ahmed et al. "A Survey of COVID-19 Contact Tracing
Apps", IEEEAccess, 8: 134577-134601, Published Online Jul. 20, 2020.
Ahmed et al. "Coronavirus Disease 2019 (COVID-19)
Complicated by Acute Respiratory Distress Syndrome: An Internist's
Perspective", Cureus, 12(3): e7482-1-e7482-7, Published Online Mar. 31,
2020.
Anderson et al. "Challenges in Creating Herd
Immunity to SARS-CoV-2 Infection by Mass Vaccination", The Lancet,
396(10263): 1616-1618, Published Online Nov. 4, 2020.
Anglemyer et al. "Digital Contact Tracing
Technologies in Epidemics: A Rapid Review", Cochrane Database of
Systematic Reviews, 2020(8/Art.No. CD013699): 1-45, Aug. 18, 2020.
Aspnes et al. "Inoculation Strategies for Victims
of Viruses and the Sum-of-Squares Partition Problem", Journal of
Computer and System Sciences, 72(6): 1077-1093, Available Online Apr.
17, 2006.
Barabasi et al. "Emergence of Scaling in Random Networks", Science, 286(5439): 509-513, Oct. 15, 1999.
Barrett et al. "Generation and Analysis of Large
Synthetic Social Contact Networks", Proceedings of the 2009 Winter
Simulation Conference, WSC, Austin TX, USA, Dec. 13-16, 2009, p. 1-12,
Dec. 13, 2009.
Beckman et al. "Creating Synthetic Baseline
Populations", Transportation Research Part A: Policy and Practice,
30(6): 415-429, Nov. 1996.
Ben Tovim "The Ministry of Health Stops the
Development of the ‘Shields’: It Prefers to Force You to Install A New
Application", Geek Time, p. 1-9, Dec. 1, 2020.
Bertsimas et al. "Optimizing Vaccine Allocation to
Combat the COVID-19 Pandemic", MedRxiv Preprint, p. 1-27, Posted Nov.
18, 2020.
Bollobas et al. "Robustness and Vulnerability of
Scale-Free Random Graphs", Internet Mathematics, 1(1): 1-35, Jan. 2004.
Britton et al. "A Mathematical Model Reveals the
Influence of Population Heterogeneity on Herd Immunity to SARS-CoV-2",
Science, 369(6505): 846-849, Aug. 14, 2020.
Bubar et al. "Model-Informed COVID-19 Vaccine
Prioritization Strategies by Age and Serostatus", Science, 371(6532):
916-921, Feb. 26, 2021.
Buckner et al. "Optimal Dynamic Prioritization of
Scarce COVID-19 Vaccines", MedRxiv Preprint, p. 1-37, Sep. 22, 2020.
Cattuto et al. "Dynamics of Person-to-Person
Interactions From Distributed RFID Sensor Networks", PLoS One, 5(&):
e11596-1-e11596-9, Jul. 15, 2010.
CDC "COVID-19 Pandemic Planning Scenarios",
Centers for Disease Control and Prevention, CDC, Office of the Assistant
Secretary for Preparedness and Response, ASPR, p. 1-9, Updated Sep. 10,
2020.
CDC "COVID-19 Pandemic Planning Scenarios",
Centers for Disease Control and Prevention, CDC, p. 1-9, Updated Mar.
19, 2021.
Centre for Time Use Research "Multinational Time Use Study", Centre for Time Use Research, p. 1-3, 2021.
Chan et al. "PACT: Privacy-Sensitive Protocols and
Mechanisms for Mobile Contact Tracing", ArXiv Preprint
ArXiv:2004.03544v4, p. 1-22, May 7, 2020.
Chen et al. "Medical Costs of Keeping the US
Economy Open During COVID-19", Scientific Reports, 10(1):
18422-1-18422-10, Oct. 28, 2020.
Chen et al. "Next Generation Technology for
Epidemic Prevention and Control: Data-Driven Contact Tracking", IEEE
Access, 7: 2633-2642, Dec. 24, 2018.
Cohen et al. "Efficient Immunization Strategies
for Computer Networks and Populations", ArXiv Preprint
ArXiv:Cond-Mat/0207387v3, p. 1-5, Dec. 10, 2003.
Cox "The Vulnerable Can Wait. Vaccinate the
Super-Spreaders First. Who Gets Priority When Covid-19 Shots Are in
Short Supply? Network Theorists Have A Counterintuitive Answer: Start
With the Sociel Butterflies", Wired, Avid Reader Press, p. 1-20, Dec.
2020-Jan. 2021.
Deming et al. "On A Least Squares Adjustment of A
Sampled Frequency Table When the Expected Marginal Totals Are Known",
The Annals of Mathematical Statistics, 11(4): 427-444, Dec. 1940.
Draief et al. "Thresholds for Virus Spread on
Networks", The Annals of Applied Probability, 18(2): 359-378, Apr. 2008.
Eames et al. "Epidemic Prediction and Control in
Weighted Networks", Epidemics, 1(1): 70-76, Published Online Dec. 25,
2008.
Eubank et al. "Modelling Disease Outbreaks in
Realistic Urban Social Networks", Nature, 429(6988): 180-184, May 13,
2004.
Eubank et al. "Structure of Social Contact
Networks and Their Impact on Epidemics", Discrete Methods in
Epidemiology, DIMACS Series Diescrete Mathematics and Theoretical
Computer Science, American Mathematic Society, 70: 181-1-181-32, 2021.
Ferretti et al. "Quantifying SARS-CoV-2
Transmission Suggests Epidemic Control With Digital Contact Tracing",
Science, 368(6491): eabb6936-1-eabb6936-7, Published Online Mar. 31,
2020.
FHWA "National Household Travel Survey", U.S.
Department of Transportation, Federal Highway Administration, FHWA, 2
P., Mar. 29, 2021.
Foy et al. "Comparing COVID-19 Vaccine Allocation
Strategies in India: A Mathematical Modelling Study", International
Journal of Infectious Diseases, 103: 431-438, Published Online Dec. 31,
2020.
Ganesh et al. "The Effect of Network Topology on
the Spread of Epidemics", Proceedings IEEE 24th Annual Joint Conference
of the IEEE Computer and Communications Societies, Miami, FL, USA, Mar.
2005, p. 1-12, Mar. 13, 2005.
Gayle et al. "Framework for Equitable Allocation
of COVID-19 Vaccine", Committee of Equitable Allocation of Vaccine for
the Novel Coronavirus, Board on Health Sciences Policy, Board on
Population Health and Public Health Practice, Health and Medicine
Division, A Consensus Study Report of The National Academies of
Sciences—Engineering—Medicine, National Academy of Medicine, p. i-xx,
1-252, 2020.
Genois et al. "Can Co-Location Be Used as A Proxy
for Face-to-Face Contacts?", EPJ Data Science, 7(1): 11-1-11-18, Dec.
2018.
Germann et al. "Mitigation Strategies for Pandemic
Influenza in the United States", Proc. Natl. Acad. Sci. USA, PNAS,
103(15): 5935-5940, Apr. 11, 2006.
Gillespie "A General Method for Numerically
Simulating the Stochastic Time Evolution of Coupled Chemical Reactions",
Journal of Computational Physics, 22(4): 403-434, Dec. 1976.
GitHub Microsoft "Computer Generated Building
Footprints for the United States", GitHub-Microsoft / USBuilding
Footprints, Version v2.0, p. 1-9, Jul. 13, 2018.
GitHub Nytimes "An Ongoing Repository of Data on
Coronavirus Cases and Deaths in the U.S.", GutHub Nytimes / Covid-19
Data, The New York Times, p. 1-12, 2021.
Halloran et al. "Modeling Targeted Layered
Containment of An Influenza Pandemic in the United States", Proc. Natl.
Acad. Sci. USA, PNAS, 105(12): 4639-4644, Mar. 25, 2008.
Hayrapetyan et al. "Unbalanced Graph Cuts",
ESA'05, Proceedings of the 13th Annual European Conference on
Algorithms, LNCS, 3669: 191-202, Oct. 3, 2005.
Hogan et al. "Report 33: Modelling the Allocation
and Impact of A COVID-19 Vaccine", Imperial College London, p. 1-21,
Sep. 25, 2020.
IBM Research Editorial Staff "Tracking
Tuberculosis in South Africa", IBM Research Blog, 7 P., Oct. 11, 2016.
Jenvald et al. "Simulation as Decision Support in
Pandemic Influenza Preparedness and Response", Proceedings of the
Conference on Intelligent Human Computer Systems for Crisis Response and
Management 2007, ISCRAM 2007, p. 295-304, May 13, 2007.
Kretschmar et al. "Impact of Delays on
Effectiveness of Contact Tracing Strategies for COVID-19: A Modelling
Study", Lancet Public Health, 5(8): e452-e459, Published Online Jul. 16,
2020.
Kumar et al. "Existence Theorems and Approximation
Algorithms for Generalized Network Security Games", 2010 IEEE 30th
International Conference on Distributed Computing Systems, Genoa, Italy,
Jun. 21-25, 2010, p. 1-19, Jun. 21, 2010.
Lipsitch et al. "Understanding COVID-19 Vaccine
Efficacy: Vaccine Efficacy in High-Risk Groups and reduced Viral
Shedding Are Important for Protection", Science, 370(6518): 763-766,
Nov. 13, 2020.
Lofgren et al. "Opinion: Mathematical Models: A
Key Tool for Outbreak Response", Proc. Natl. Acad. Sci. USA, PNAS,
111(51): 18095-18096, Dec. 23, 2014. & Correction, 112(2): E234,
Jan. 13, 2015.
Longini Jr. et al. "Containing Pandemic Influenza
With Antiviral Agents", American Journal of Epidemiology, 159(7):
623-633, Apr. 1, 2004.
Lum et al. "A Two-Stage, Fitted Values Approach to
Activity Matching", International Journal of Transportation, 4(1):
41-56, 2016.
Machi et al. "Scalable Epidemiological Workflows
to Support COVID-19 Planning and Response", MedRxiv Preprint, p. 1-19,
Posted Online Feb. 26, 2021.
Marathe et al. "Computational Epidemiology", KDD
'14, Proceedings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, New York City, NY, USA, Aug. 24-27,
2014, Slide Show, p. 1-209, Aug. 24, 2014. (Part I).
Marathe et al. "Computational Epidemiology", KDD
'14, Proceedings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, New York City, NY, USA, Aug. 24-27,
2014, Slide Show, p. 1-209, Aug. 24, 2014. (Part II).
Marathe et al. "Computational Epidemiology", KDD
'14, Proceedings of the 20th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, New York City, NY, USA, Aug. 24-27,
2014, Slide Show, p. 1-209, Aug. 24, 2014. (Part III).
Matrajt et al. "Vaccine Optimization for COVID-19:
Who to Vaccinate First?", MedRxiv Preprint, p. 1-84, Posted Nov. 10,
2020.
May et al. "Spatial Heterogeneity and the Design
of Immunization Programs", Mathematical Biosciences, 72(1): 83-111, Nov.
1984.
Medlock "Optimizing Influenza Vaccine
Distribution", Clemson University, Department of Mathematical Sciences,
p. 1-29, Aug. 3, 2009.
Morawska et al. "Airborne Transmission of
SARS-CoV-2: The World Should Face the Reality", Environmental
International, 139: 105730-1-105730-4, Available Online Apr. 10, 2020.
Office Action and Search Report dated Dec. 17,
2020 From the Israel Patent Office Re. Application No. 276648. (9
Pages.).
Office Action and Search Report dated Dec. 17,
2020 From the Israel Patent Office Re. Application No. 276665. (9
Pages).
Office Action and Search Report dated Dec. 17,
2020 From the Israel Patent Office Re. Application No. 277083. (10
Pages).
Pastor-Satorras et al. "Epidemic Processes in
Complex Networks", ARXiv Preprint ArXiv:1408.2701v2, p. 1-62, Sep. 18,
2015.
Pastor-Satorras et al. "Immunization of Complex
Networks", ArXiv Preprint ArXiv:Cond-Math/010766v2, p. 1-9, Apr. 11,
2002.
Preciado et al. "Optimal Resource Allocation for
Networks Protection Against Spreading Processes", ArXiv Preprint
ArXiv:1309.6270v2, p. 1-10, May 11, 2014.
Preciado et al. "Optimal Vaccine Allocation to
Control Epidemic Outbreaks in Arbitrary Networks", ArXiv Preprint
ArXiv:1303.3984v1, p. 1-8, Mar. 16, 2013.
Qian et al. "Ventilation Control for Airborne
Transmission of Human Exhaled Bio-Aerosols in Buildings", Journal of
Thoracic Disease, 10(Supp1.19): S2295-S2304, Jul. 2018.
Saad-Roy et al. "Immune Life History, Vaccination,
and the Dynamics of SARS-CoV-2 Over the Next 5 Years", Science,
370(6518): 811-818, Nov. 13, 2020.
Saha et al. "Approximation Algorithms for Reducing
the Spectral Radius to Control Epidemic Spread", ArXiv Preprint
ArXiv:1501.0661v1, p. 1-14, Jan. 26, 2015.
Sambaturu et al. "Designing Effective and
Practical Interventions to Contain Epidemics", Proceedings of the 19th
International Conference on Autonomous Agents and MultiAgent Systems,
AAMAS 2020, Auckland, New Zealand, May 9-13, 2020, p. 1187-1195, May 9,
2020.
Skene et al. "A Marginal Benefit Approach for
Vaccinating Influenza ‘Superspreaders’", Medical Decision Making, 34(4):
536-549, May 2014.
Straetemans et al. "Priorization Strategies for
Pandemic Influenza Vaccine in 27 Countries of the European Union and the
Global Health Security Action Group: A Review", BMC Public Health,
7(1): 236-1-236-12, Published Online Sep. 7, 2007.
Tennenholtz et al. "Sequential Vaccination for
Containing Epidemics", MedRxiv Preprint, p. 1-16, Posted Apr. 14, 2020.
Tong et al. "Gelling, and Melting, Large Graphs by
Edge Manipulation", Proceedings of the 21st ACM International
Conference on Information and Knowledge Management, CIKM'12, Maui, HI,
USA, Oct. 29-Nov. 2, 2012, p. 245-254, Oct. 29, 2012.
Translation Dated Jan. 3, 2021 of Office Action
dated Dec. 17, 2020 From the Israel Patent Office Re. Application No.
276665. (5 Pages).
Translation Dated Jan. 3, 2021 of Office Action
dated Dec. 17, 2020 From the Israel Patent Office Re. Application No.
277083. (5 Pages).
Translation Dated Jan. 3, 2021 of Office Action
Report dated Dec. 17, 2020 From the Israel Patent Office Re. Application
No. 276648. (5 Pages).
U.S Bureau of Labor Statistics "American Time Use
Survey (ATUS)", U.S Bureau of Labor Statstics, p. 1-4, Jun. 25, 2020.
U.S. Census Bureau "American Community Survey
2013-2017 5-Year Data Release: Median Household Income, Poverty Rates
and Computer and Internet Use", U.S Census Bureau, Press Release, 7 P.,
Dec. 6, 2018.
Van Miegham et al. "Virus Spread in Networks",
IEEE/ACM Transactions on Networking, 17(1): 1-14, Published Online Jun.
24, 2008.
Van Mieghem et al. "Decreasing the Spectral Radius
of a Graph by Link Removals", Physical Review E, 84(1):
016101-1-016101-12, Jul. 6, 2011.
Venkatramanan et al. "Optimizing Spatial
Allocation of Seasonal Influenxa Vaccine Under Temporal Constraints",
PLoS One, Computational Biology, 15(9): e1007111-1-e1007111-17, Sep. 16,
2019.
Venkatramanan et al. "Optimizing Spatial
Allocation of Seasonal Influenza Vaccine Under Temporal Constraints",
PLoS Computational Biology, 15(9): e1007111-1-e1007111-17, Published
Online Sep. 16, 2019.
Venkatramanan et al. "Spatio-Temporal Optimization
of Seasonal Vaccination Using A Metapopulation Model of Influenza",
2017 IEEE INternational Conference on Healthcare Informatics, ICHI, Park
City, UT, USA, Aug. 23-26, 2017, p. 134-144, Aug. 23, 2017.
Versel "Smartphone App Seeks ‘Superspreaders’ of Flu", MobileHealthNews, 3 P., Apr. 26, 2011.
Who "Draft Landscape of COVID-19 Candidate Vaccines", 9 P., Aug. 10, 2020.
Wilder et al. "Preventing Infectious Disease in
Dynamic Populations Under Uncertainty", The Thirty-Second AAAI
Conference on Artificial Intelligence, Computational Sustainability and
Artificial Intelligence, AAAI-18, 32(1): 841-848, Apr. 25, 2018.
Yang et al. "Efficient Vaccination Strategies for
Epidemic Control Using Network Information", Epidemics, 27: 115-122,
Published Online Mar. 6, 2019.
Zhang et al. "Near-Optimal Algorithms for
Controlling Propagation at Group Scale on Networks", IEEE Transactions
on Knowledge and Data Engineering, 28(12): 3339-3352, Published Online
Sep. 1, 2016.
* Cited by examiner, † Cited by third party
Cited By (6)
Publication number
Priority date
Publication date
Assignee
Title
US11348690B2
*
2020-08-11
2022-05-31
Gal EHRLICH
Methods
and systems of prioritizing treatments, vaccination, testing and/or
activities while protecting the privacy of individuals
Family To Family Citations
US11423755B2
*
2017-05-17
2022-08-23
Blue Storm Media, Inc.
System and method for a digital proof of vaccine
KR20220082567A
*
2020-12-10
2022-06-17
주식회사 코너스
Real time
space intelligence delivery system and method based on situational
awareness that matches object recognition from cctv video to 3d space
coordinates
US20220375624A1
*
2021-05-18
2022-11-24
At&T Intellectual Property I, L.P.
Systems, apparatus and methods of real-time cellular communication assisted vaccine tracking system
CN113643785A
*
2021-07-22
2021-11-12
海南大学
DIKW atlas-based vaccination concentration confirmation method
WO2023013125A1
*
2021-08-05
2023-02-09
Nec Corporation
Method and apparatus for cancelling anonymization for an area including a target
* Cited by examiner, † Cited by third party, ‡ Family to family citation
Similar Documents
Publication
Publication Date
Title
US11107588B2
2021-08-31
Methods
and systems of prioritizing treatments, vaccination, testing and/or
activities while protecting the privacy of individuals
Mandolo et al.
2021
SARS-CoV-2
exposure in Malawian blood donors: an analysis of seroprevalence and
variant dynamics between January 2020 and July 2021
Choudhury et al.
2021
Covidchain: An anonymity preserving blockchain based framework for protection against covid-19
Tsoi et al.
2021
The way forward after COVID-19 vaccination: vaccine passports with blockchain to protect personal privacy
Cook et al.
2012
Modern contact investigation methods for enhancing tuberculosis control in aboriginal communities
Dickson et al.
2020
Do point-of-care tests (POCTs) offer a new paradigm for the management of patients with influenza?
Tibbetts
2020
Researchers Continue Quest to Contain Spread of COVID-19: Digital technologies aim to accelerate contact tracing.
McCarthy et al.
2011
Monitoring vaccine safety using the Vaccine Safety Datalink: utilizing immunization registries for pandemic influenza
Truszkowska et al.
2022
Urban Determinants of COVID-19 Spread: a Comparative Study across Three Cities in New York State
Ljungholm et al.
2021
Implementing digital vaccine passports to control the spread of COVID-19: law, rights, and ethics
Torner et al.
2015
Implication
of health care personnel in measles transmission: The need for updated
immunization status in the move towards eradication of measles in
Catalonia
Kim et al.
2022
Social
distancing and mask-wearing could avoid recurrent stay-at-home
restrictions during COVID-19 respiratory pandemic in New York City
Exeter et al.
2020
Going Hard and Going Early in New Zealand: The" Team of 5 Million" Unites Against COVID-19
Alanezi
2021
Applications of Internet of things (IoT) and information systems in managing pandemics: a case study of COVID-19
Priority And Related Applications
Child Applications (1)
Application
Priority date
Filing date
Relation
Title
US17/386,606
2020-08-11
2021-07-28
Continuation
Methods
and systems of prioritizing treatments, vaccination, testing and/or
activities while protecting the privacy of individuals
Priority Applications (3)
Application
Priority date
Filing date
Title
PCT/IL2021/050903
2020-08-11
2021-07-27
Methods and systems of prioritizing treatments,
vaccination, testing and/or activities while protecting the privacy of
individuals
US17/386,606
2020-08-11
2021-07-28
Methods and systems of prioritizing treatments,
vaccination, testing and/or activities while protecting the privacy of
individuals
IL285476A
2020-08-11
2021-08-09
Methods and systems of prioritizing treatments,
vaccination, testing and/or activities while protecting the privacy of
individuals
Applications Claiming Priority (6)
Application
Filing date
Title
IL276665
2020-08-11
IL276648
2020-08-11
IL277083A
2020-09-01
Methods
and systems of prioritizing treatments, vaccination, testing and/or
activities while protecting the privacy of individuals
IL277083
2020-09-01
Legal Events
Date
Code
Title
Description
2020-11-30
FEPP
Fee payment procedure
Free format text: ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY
2020-12-08
FEPP
Fee payment procedure
Free format text: ENTITY STATUS SET TO SMALL (ORIGINAL EVENT CODE: SMAL); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY
2021-04-30
STPP
Information on status: patent application and granting procedure in general
Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER
2021-05-21
STPP
Information on status: patent application and granting procedure in general
Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS
2021-08-11
STCF
Information on status: patent grant
Free format text: PATENTED CASE
Concepts
Download
Name
Image
Sections
Count
Query match
vaccination
title,claims,description
158
0.000
effects
title,claims,description
58
0.000
protecting
title,description
16
0.000
diseases by infectious agent
claims,abstract,description
122
0.000
vaccines
claims,description
150
0.000
disease
claims,description
100
0.000
health
claims,description
62
0.000
memory
claims,description
40
0.000
biological transmission
claims,description
38
0.000
Coronaviridae
claims,description
28
0.000
simulation
claims,description
26
0.000
pathogenic
abstract,description
190
0.000
Comentarii
Trimiteți un comentariu