WO2021191783A1

WO2021191783A1 - Epidemic-monitoring system

Info

Publication number: WO2021191783A1
Application number: PCT/IB2021/052383
Authority: WO
Inventors: Yonatan AMIR
Original assignee: Diagnostic Robotics Ltd.
Priority date: 2020-03-24
Filing date: 2021-03-23
Publication date: 2021-09-30

Abstract

Apparatus and methods for monitoring an epidemiological disease are described. An epidemic-monitoring system (20) is driven to receive data from patients, via patient-interface devices (22). Based upon the received data, the epidemic-monitoring system (20) analyzes data to determine current trends of the epidemiological disease, on a location-by-location basis, and at least partially in response thereto, forecasts cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future. An output is generated indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future. Other applications are also described.

Description

EPIDEMIC-MONITORING SYSTEM

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from:

US Provisional patent application 62/993,792 to Amir, filed March 24, 2020, entitled "Epidemic monitoring system," and

US Provisional patent application 63/001,590 to Amir, filed March 30, 2020, entitled "Epidemic monitoring system."

Both of the above-referenced US Provisional applications are incorporated herein by reference.

FIELD OF EMBODIMENTS OF THE INVENTION

Some applications of the present invention relates to medical apparatus and methods, and particularly to apparatus and methods for risk-assessment and monitoring of an epidemic.

BACKGROUND

An epidemic is a rise in the number of cases of a disease beyond what is normally expected in a geographical area, according to the Centers for Disease Control and Prevention. Frequently, the rise in cases happens quickly. Examples of epidemics that have occurred in the twenty-first century include the severe acute respiratory syndrome (SARS) virus that spread in Asia beginning in 2003, the Ebola outbreak that occurred in 2014-2016 in West Africa, and the Zika virus outbreak that occurred in the Americas in 2015-2016.

A pandemic is used to describe a disease that has spread across many countries and affects a large number of people. An example of a pandemic is the COVID-19 pandemic, which is caused by the SARS-CoV-2 virus.

SUMMARY OF EMBODIMENTS

In accordance with some applications of the present invention, an epidemic - monitoring system includes a plurality of patient interfaces, via which respective patients and potential patients interact with the system. For some applications, the system additionally includes a triage-center interface via which a triage center interacts with the system. For example, the triage center may include a nationwide, statewide, or regional emergency monitoring center which may be a physical or a virtual monitoring center). Typically, the system additionally includes a caregiver interface, via which a caregiver can access data relating to specific patients, and/or interact with specific patients. For some applications, the system further includes a health- authority interface via which a health authority (such as, an international, nationwide, statewide, or regional health authority) can access the system. Alternatively or additionally other governmental authorities can access the system via the health authority interface. Typically, the health authority is able to determine macro-level information regarding the spread of an epidemic within respective areas by accessing data from the system. Further typically, the health authority is then able to provide instructions and/or information to respective agents that interact with the system based on a combination of the macro-level information and patient-specific information, as described in further detail hereinbelow.

For some applications, the patient interface initially guides the patient through a questionnaire, which may include questions relating to the patient's age, sex, recent travel, current symptoms, general health status, current quarantine status, test status (i.e., have they undergone a test to check whether they are infected by the epidemic) etc. For some applications, the patient interface additionally performs a test on the patient.

In accordance with respective applications, the patient may choose to initiate interaction with the system based on their own initiative (e.g., because they are experiencing symptoms, because they suffer from a condition which puts them at risk, or because of recent travel), or the patient may be requested to initiate interaction with the system by a health authority or other governmental authority. For example, at a time of an epidemic, a health authority may request that all citizens and/or residents interact with the system, or that a certain subset of citizens and/or residents (e.g., based on age, health status, travel, proximity to positively-identified infected patients, proximity to high-risk patients) interact with the system. Typically, based on the patient's responses to the questionnaire, the system outputs an initial assessment of the patient's risk status. Further typically, the system outputs recommendations and/or instructions to the patient. By way of example, if a patient is high risk, the system may generate an output indicating that it is forbidden for the patient to leave their home for a given time period. Alternatively or additionally, the system may recommend to the patient or instruct the patient to undergo a test in order to make a positive determination of whether the patient is suffering from the epidemic. Further alternatively or additionally, the system may automatically flag this patient at the triage-center interface, as described in further detail hereinbelow.

For some applications, in response to determining that a patient is above a certain level of risk or that the patient has been positively identified as being infected, the system may take steps to protect other people who may have interacted with the patient. For example, the system may output a recommendation and/or instruction that other members of the patient's household undergo an initial assessment with the system, or the system may automatically instruct other members of the patient's household to undergo an initial assessment with the system (e.g., via electronic messages that are sent to other members of the high-risk patient's household). For some applications, in response to determining that a patient is above a certain level of risk or that the patient has been positively identified as being infected, the system requests that the patient input information regarding her/his activities and/or locations over a time period over which the patient is suspected of having been infected. Alternatively or additionally, the system may automatically access information relating the patient's activities and/or locations over this time period, e.g., by accessing data on the patient's smartphone. For some applications, the above-described steps are only performed once the patient is positively confirmed as being infected, based on a diagnostic test.

For some applications, the system includes information regarding other members of the patient's household, interactions between the patient and other people, and/or locations of the patient over a given time period. As described hereinabove, for some applications, the system requests that the patient input information regarding her/his activities and/or locations over a time period over which the patient is suspected of having been infected. Alternatively or additionally, the system may automatically access information relating the patient's activities and/or locations over this time period, e.g., by accessing data on the patient's smartphone. For some applications, based upon this information, the system builds an epidemiological map indicating a spread of the epidemic at respective areas. Typically, such information is used to guide recommendations to members of the public within respective areas, by the health authority. For example, in areas that are at high risk, the health authority may issue instructions that all members of the public must remain under quarantine, whereas in areas that are at lower risk, they may advise only at-risk members of the population to remain indoors, and in areas that are at very low risk, they may allow all regular activities to continue. There is therefore provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from patients, via patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease, on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

In some applications, driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future includes driving the epidemic -monitoring system to run a vector autoregression algorithm to predict the spread of the epidemiological disease at respective locations based upon data regarding the spread of the epidemiological disease at respective locations to date.

In some applications, driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future includes driving the epidemic -monitoring system to account for a given patient's locations over a time period over which the patient is suspected of having been infected with the epidemiological disease.

In some applications, driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future includes driving the epidemic-monitoring system to account for one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

In some applications, driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future includes driving the epidemic-monitoring system to account for one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

In some applications, driving the epidemic -monitoring system to generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations at the given time in the future includes driving the epidemic -monitoring system to generate a predicted future heatmap indicating the predicted spread of the epidemiological disease at different locations at a given time in the future.

In some applications, driving the epidemic-monitoring system to generate the predicted future heatmap includes driving the epidemic-monitoring system to generate a predicted future heatmap that allows a user to scroll through different times in the future, and observe how the heatmap changes over time.

In some applications, the method further includes driving the epidemic-monitoring system to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast.

In some applications, driving the epidemic-monitoring system to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast includes driving the epidemic -monitoring system to cross-check locations at which the given patient was known to have been present against the forecasts for the locations.

In some applications, the method further includes driving the epidemic-monitoring system to generate an alert that the given patient should undergo a diagnostic test, based upon the predicted probability.

In some applications, the method further includes driving the epidemic-monitoring system to generate an alert that the given patient should enter quarantine, based upon the predicted probability.

In some applications, the method further includes driving the epidemic-monitoring system to generate an alert that family members of the given patient should enter quarantine, based upon the predicted probability.

In some applications, the method further includes driving the epidemic-monitoring system to generate an alert that people who came into proximity with the given patient should enter quarantine, based upon the predicted probability. There is further provided, in accordance with some applications of the present invention, apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus including: an epidemic-monitoring system including one or more computer processors that are configured to: receive data from patients, via the patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease, on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

In some applications, the epidemic -monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by running a vector autoregression algorithm to predict the spread of the epidemiological disease at respective locations based upon data regarding the spread of the epidemiological disease at respective locations to date.

In some applications, the epidemic -monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for a given patient's locations over a time period over which the patient is suspected of having been infected with the epidemiological disease.

In some applications, the epidemic -monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

In some applications, the epidemic -monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

In some applications, the epidemic-monitoring system is configured to generate a predicted future heatmap indicating the predicted spread of the epidemiological disease at different locations at a given time in the future.

In some applications, the epidemic-monitoring system is configured to generate a predicted future heatmap that allows a user to scroll through different times in the future, and observe how the heatmap changes over time.

In some applications, the epidemic-monitoring system is configured to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast.

In some applications, the epidemic-monitoring system is configured to predict a probability of the given patient contracting the epidemiological disease by cross-checking locations at which the given patient was known to have been present against the forecasts for the locations.

In some applications, the epidemic-monitoring system is configured to generate an alert that the given patient should undergo a diagnostic test, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that the given patient should enter quarantine, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that family members of the given patient should enter quarantine, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that people who were in proximity to the given patient should enter quarantine, based upon the predicted probability.

There is further provided, in accordance with some applications of the present invention, a computer software product for monitoring an epidemiological disease and for use with patient-interface device and including a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic -monitoring system to: receive data from patients, via the patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease, on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

There is further provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from a plurality of patients, via patient-interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability.

In some applications, driving the epidemic-monitoring system to predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information includes driving the epidemic- monitoring system to predict a probability of the given patient contracting the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

In some applications, driving the epidemic-monitoring system to predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information includes driving the epidemic- monitoring system to predict a probability of the given patient contracting the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

In some applications, driving the epidemic -monitoring system to generate an output based upon the predicted probability includes driving the epidemic-monitoring system to generate an alert that the patient should undergo a diagnostic test, based upon the predicted probability.

In some applications, driving the epidemic -monitoring system to generate an output based upon the predicted probability includes driving the epidemic-monitoring system to generate an alert that the patient should enter quarantine, based upon the predicted probability.

In some applications, driving the epidemic -monitoring system to generate an output based upon the predicted probability includes driving the epidemic-monitoring system to generate an alert that family members of the patient should enter quarantine, based upon the predicted probability.

In some applications, driving the epidemic -monitoring system to generate an output based upon the predicted possibility includes driving the epidemic -monitoring system to generate an alert that people who were in proximity to the patient should enter quarantine, based upon the predicted probability.

There is further provided, in accordance with some applications of the present invention, apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus including: an epidemic-monitoring system including one or more computer processors that are configured to: receive data from patients, via the patient- interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability. In some applications, the epidemic-monitoring system is configured to predict the probability of the given patient contracting or having contracted the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

In some applications, the epidemic-monitoring system is configured to predict the probability of a given patient contracting or having contracted the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

In some applications, the epidemic-monitoring system is configured to generate an alert that the patient should undergo a diagnostic test, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that the patient should enter quarantine, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that family members of the patient should enter quarantine, based upon the predicted probability.

In some applications, the epidemic-monitoring system is configured to generate an alert that people who were in proximity to the patient should enter quarantine, based upon the predicted probability.

There is further provided, in accordance with some applications of the present invention, a computer software product for monitoring an epidemiological disease and for use with patient-interface devices and including a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients, via the patient- interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability.

There is further provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient-interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus including: an epidemic-monitoring system including one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient- interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, a computer software product for monitoring an epidemiological disease and for use with patient-interface devices and including a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via the patient- interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient-interface devices; receive an indication of patients that were determined to be suffering from the epidemiological disease using diagnostic tests; in response thereto, determining correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease; and subsequently, determine a likelihood of a given patient suffering from the epidemiological disease at least partially based upon the determined correlations.

There is further provided, in accordance with some applications of the present invention, apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus including: an epidemic-monitoring system including one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient- interface devices; receive an indication of patients that were determined to be suffering from the epidemiological disease using diagnostic tests; in response thereto, determine correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease; and subsequently determine a likelihood of a given patient suffering from the disease at least partially based upon the determined correlations.

There is further provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus including: an epidemic-monitoring system including one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, a computer software product for monitoring an epidemiological disease and for use with patient-interface devices and including a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via the patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

There is further provided, in accordance with some applications of the present invention, a method for monitoring an epidemiological disease including: driving an epidemic-monitoring system to: receive data from patients and potential patients, via patient interface devices; based upon the received data: output recommendations to patients and potential patients, via the patient- interface devices; triage patients and potential patients, via a triage-center interface; and generate an epidemiological map indicating a spread of the epidemiological disease.

The present invention will be more fully understood from the following detailed description of applications thereof, taken together with the drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a block diagram showing various interfaces that are used in an epidemic monitoring system, in accordance with some applications of the present invention;

Fig. 2 is a schematic illustration of an example of a patient interface that is used for allowing a patient or a potential patient to input information into, and retrieve information from, the epidemic-monitoring system, in accordance with some applications of the present invention;

Fig. 3A is a schematic illustration of an example of a dashboard of patients as shown on a triage-center interface, in accordance with some applications of the present invention;

Fig. 3B is a schematic illustration of an example of a medical summary and other relevant information relating to a specific patient, as shown on a caregiver interface, in accordance with some applications of the present invention;

Fig. 3C is a schematic illustration of how the system derives a patient's risk profile and provides next-step guidance, based on a patient's symptoms, medical history, and/or other patient-related data, in accordance with some applications of the present invention;

Fig. 3D is a schematic illustration of a natural-language-processing algorithm that is used to derive a patient's risk profile and/or other determinations, based on a patient's symptoms, medical history, and/or other patient-related data, in accordance with some applications of the present invention; Fig. 4A is a schematic illustration of a first step in a location-related risk assessment algorithm, as it appears on a patient interface, in accordance with some applications of the present invention;

Fig. 4B which is an example of a heatmap that shows risk of exposure in a vicinity of the patient, in accordance with some applications of the present invention;

Fig. 5 shows an example of a table indicating trends, on a location-by-location basis, that is generated in accordance with some applications of the present invention;

Fig. 6 shows a set of curves, each of which represents, for a respective city, a plot of a probability of a patient contracting the disease against a number of days into the future, in accordance with some applications of the present invention;

Fig. 7 shows a predicted heatmap, indicating numbers of predicted cases at a future time, in accordance with some applications of the present invention;

Fig. 8 is a schematic illustration of a forecast for the spread of an epidemic by location, that is performed by an epidemic -monitoring system, in accordance with some applications of the present invention; and

Fig. 9 is an example of a table showing a predicted number of additional cases at a given time in the future, and a percentage change in the number of cases, on a location-by- location basis, that is generated in accordance with some applications of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Reference is now made to Fig. 1, which is a block diagram showing various interfaces that are used in an epidemic-monitoring system 20, in accordance with some applications of the present invention. As shown in Fig. 1, typically, the epidemic -monitoring system include a plurality of patient interfaces 22, via which respective patients and potential patients interact with the system. Typically, each of the patient interfaces comprises a program or an application that is run on an electronic device that includes a user interface and a computer processor, such as a home computer, a laptop, a tablet computer, a smartphone, a smartwatch, and/or a different electronic device. For some applications, the system additionally includes a triage-center interface 24 via which a triage center interacts with the system. For example, the triage center may include a nationwide, statewide, or regional emergency monitoring center (which may be a physical or a virtual monitoring center). For some applications, the monitoring center is designated for the use of paying users of the system. Typically, the system additionally includes a caregiver interface 26 via which a caregiver can access data relating to specific patients, and/or interact with specific patients. For some applications, the system further includes a health-authority interface 28 via which a health authority (such as, an international, nationwide, statewide, or regional health authority) can access the system. Alternatively or additionally other governmental authorities can access the system via the health-authority interface. Typically, the health authority is able to determine macro-level information regarding the spread of an epidemic within respective areas by accessing data from the system. Further typically, the health authority is then able to provide instructions and/or information to respective agents that interact with the system based on a combination of the macro-level information and patient-specific information, as described in further detail hereinbelow.

Reference is now made to Fig. 2, which is a schematic illustration of an example of patient interface 22, which is used for allowing a patient, or a potential patient, to input information into, and retrieve information from, epidemic-monitoring system 20, in accordance with some applications of the present invention. Typically, the patient interface comprises a patient-interface device. Typically, a program or an application is run on an electronic device (i.e., the patient- interface device) that includes a user interface and a computer processor, such as a home computer, a laptop, a tablet computer, a smartphone, a smartwatch, and/or a different electronic device. In the example shown in Fig. 2, the patient interface device is a smartphone upon which an application or a program is run. The terms "patient interface" and "patient-interface device" are used interchangeably in the present application.

Fig. 2 shows a set of screen shots from an initial interaction of a patient (i.e., a patient or a potential patient) with the epidemic -monitoring system. As shown, for some applications, the patient interface initially guides the patient through a questionnaire, which may include questions relating to the patient's age, sex, recent travel, current symptoms, general health status, current quarantine status, test status (i.e., have they undergone a test to check whether they are infected by the epidemic) etc. For some applications, the patient interface device additionally performs a test on the patient. For example, a smartwatch or smartphone may be used to measure the patient's pulse, and/or a thermometer (e.g., a thermometer that communicates wirelessly with the patient interface) may be used to measure the patient's temperature. In accordance with respective applications, the patient may choose to initiate interaction with the system based on their own initiative (e.g., because they are experiencing symptoms, because they suffer from a condition which puts them at risk, or because of recent travel), or the patient may be requested to initiate interaction with the system by a health authority or other governmental authority. For example, at a time of an epidemic, a health authority may request that all citizens and/or residents interact with the system, or that a certain subset of citizens and/or residents (e.g., based on age, health status, travel, proximity to positively-identified infected patients, proximity to high-risk patients) interact with the system. Typically, based on the patient's responses to the questionnaire, the system outputs an initial assessment of the patient's risk status. Further typically, the system outputs recommendations and/or instructions to the patient. By way of example, if a patient is high risk, the system may generate an output indicating that it is forbidden for the patient to leave their home for a given time period. Alternatively or additionally, the system may recommend to the patient or instruct the patient to undergo a test in order to make a positive determination of whether the patient is suffering from the epidemic. Further alternatively or additionally, the system may automatically flag this patient at the triage-center interface, as described in further detail hereinbelow.

For some applications, in response to determining that a patient is above a certain level of risk or that the patient has been positively identified as being infected, the system may take steps to protect other people who may have interacted with the patient. For example, the system may output a recommendation and/or instruction that other members of the patient's household undergo an initial assessment with the system, or the system may automatically instruct other members of the patient's household to undergo an initial assessment with the system (e.g., via electronic messages that are sent to other members of the high-risk patient's household). For some applications, in response to determining that a patient is above a certain level of risk or that the patient has been positively identified as being infected, the system requests that the patient input information regarding her/his activities and/or locations over a time period over which the patient is suspected of having been infected. Alternatively or additionally, the system may automatically access information relating the patient's activities and/or locations over this time period, e.g., by accessing data on the patient's smartphone. For some applications, the above-described steps are only performed once the patient is positively confirmed as being infected, based on a diagnostic test. For some applications, once a patient is registered within the system as having any risk, the patient is directed to complete a questionnaire at regular time intervals (e.g., daily, weekly, etc.), in order for the system to continue to monitor the patient. For some applications, the questionnaire is tailored to the patient, based on previous data received from the patient, such as the symptoms previously reported by the patient.

Reference is now made to Fig. 3A, which is a schematic illustration of an example of a dashboard of patients as shown on triage-center interface 24, in accordance with some applications of the present invention. As described hereinabove, the triage center may include a nationwide, statewide, or regional emergency monitoring center (which may be a physical or a virtual monitoring center). For some applications, the monitoring center is designated for paying users of the system. Typically, the triage-center interface comprises a program or an application that is run on an electronic device that includes a user interface and a computer processor, such as a computer, a laptop, and/or a different electronic device that is used within the triage center. Typically, triage-center interface 24 provides personnel at the triage center with a snapshot view of a relatively large number of patients, as shown. Such patients may include all patients that have interacted with the system, all patients that have interacted with the system in a given geographical region, only patients having a risk level exceeding a certain threshold, only patients who are currently in quarantine, etc.

For some applications, the patients' risk levels are automatically scored and/or flagged, for example, by analyzing all data relating to the patients using artificial-intelligence algorithms, and/or using protocols authorized by a relevant authority. By providing the personnel with a snapshot view and automatically scoring the patients, the system typically facilitates rapid decision making by the triage center personnel. For some applications, by providing the personnel with a snapshot view, and automatically scoring the patients, the system facilitates decision making regarding which patients to send for diagnostic tests, for example, based on patients' scores, as well as the current availability of tests, the proximity of patients to test stations, etc. For some applications, the triage center personnel communicate directly with certain patients, based upon those patients having been flagged, and/or having a certain score.

Reference is now made to Fig. 3B, which is a schematic illustration of an example of a medical summary and/or other relevant information relating to a specific patient, as shown on caregiver interface 26, in accordance with some applications of the present invention. For some applications, this interface is an interface that is accessible to the triage center personnel. Alternatively or additionally, the caregiver interface may be accessible to medical personnel (such as doctors, nurses, and paramedics), to whom certain patients are assigned and who typically provide more personalized care to their assigned patients, than that provided by the triage center personnel. Typically, the caregiver interface comprises a program or an application that is run on an electronic device that includes a user interface and a computer processor, such as a computer, a laptop, and/or a different electronic device that is used within the triage center, within a hospital, within a clinic, and/or within a different setting. Typically, the caregiver interface provides relevant information regarding specific patients. Such information typically includes the patient's medical history, as well as a timeline of all of the patient's interactions with the system (such that the caregiver can follow the development of the patient's symptoms). Typically, the caregiver directly interacts with the patient, in order to provide advice, for example, via electronic messages to the patient interface, and/or via a telephone call, or a video conference.

For some applications, the system includes information regarding other members of the patient's household, interactions between the patient and other people, and/or locations of the patient over a given time period. As described hereinabove, for some applications, the system requests that the patient input information regarding her/his activities and/or locations over a time period over which the patient is suspected of having been infected. Alternatively or additionally, the system may automatically access information relating the patient's activities and/or locations over this time period, e.g., by accessing data on the patient's smartphone. For some applications, based upon this information, the system builds an epidemiological map indicating a spread of the epidemic at respective areas. Typically, such information is used to guide recommendations to members of the public within respective areas, by the health authority. For example, in areas that are at high risk, the health authority may issue instructions that all members of the public must remain under quarantine, whereas in areas that are at lower risk, they may advise only at-risk members of the population to remain indoors, and in areas that are at very low risk, they may allow all regular activities to continue.

Reference is now made to Fig. 3C, which is a schematic illustration of how epidemic monitoring system 20 derives a patient's risk profile and provides next-step guidance, based on a patient's symptoms, medical history, and/or other patient-related data, in accordance with some applications of the present invention. For some applications, the system uses artificial- intelligence algorithms to extract certain positive concepts from the patient's symptoms, medical history, and/or other patient-related data. Alternatively or additionally, the system uses artificial-intelligence algorithms to negate certain concepts from the patient's symptoms, medical history, and/or other patient-related data. Typically, the artificial-intelligence algorithms map the extracted positive concepts to a certain identified ID, and classify the patient as belonging to that ID. For example, the ID may be a classification of the patient's risk profile and/or the recommended next-step guidance for the patient. For some such applications, the artificial-intelligence algorithms include a neural network (e.g., a convolutional neural network), a Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine, Naive Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, and/or a Gradient Boosting algorithm.

For some applications, the epidemic-monitoring system accesses historical medical data relating to patients, and determines the likelihood that patients are infected with the epidemiological disease based upon a combination of the patients' current symptoms and the historical medical data. For some applications, based upon the combination of the patients' current symptoms and the historical medical data, the epidemic-monitoring system generates an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease, and/or indicating that they should enter quarantine. For example, in response to determining that a subset of patients have a probability of being infected with the epidemiological disease that exceeds a threshold, the epidemic-monitoring system may generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease, and/or indicating that they should enter quarantine.

For some applications, the epidemic-monitoring system accesses patient-population historical medical data relating to all patients within the system or relating to a subset of patients. For some such applications, the system determines the likelihood that a given patient will become infected with the epidemiological disease and/or a likelihood of the given patient developing severe symptoms at least partially based upon a combination of the given patients' historical medical data and the patient-population historical medical data. For example, the epidemic-monitoring system may determine correlations between historical and/or chronic medical conditions and a likelihood of becoming infected and/or a likelihood of developing severe symptoms, by analyzing patient-population historical medical data. The epidemic monitoring system may then determine the likelihood of a given patient becoming infected and/or developing severe symptoms based upon the correlations. Reference is now made to Fig. 3D, which is a schematic illustration of a natural- language-processing algorithm that is used to derive a patient's risk profile and/or other determinations, based on a patient's symptoms, medical history, and/or other patient-related data, in accordance with some applications of the present invention. As shown in Fig. 3D, for some applications, the system accesses medical databases in order to analyze and determine connections between different symptoms, medical conditions, treatment plans, quarantine-recommendations, etc. For some applications, the system uses such natural- language-processing algorithms to derive a patient's risk profile and provides next-step guidance, based on a patient's symptoms, medical history, and/or other patient-related data.

For some applications, the system receives inputs relating to patients' symptoms, and subsequently is configured to receive an input (from the patients, the triage center, their caregivers and/or the health authority) indicating which of the patients were confirmed as suffering from the epidemiological disease (e.g., via a polymerase chain reaction test, and/or a similar test). The system then uses artificial-intelligence algorithms to determine correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease. Subsequently, in response to receiving an indication of a given patient's symptoms, the system determines a likelihood of the patient suffering from the epidemiological disease at least partially based upon the determined correlations.

For some such applications, the artificial-intelligence algorithms include a neural network (e.g., a convolutional neural network), a Linear Regression, Logistic Regression, Decision Tree, Support Vector Machine, Naive Bayes, kNN, K-Means, Random Forest, Dimensionality Reduction Algorithms, and/or Gradient Boosting algorithm. Typically, during a machine-learning stage, the algorithm is trained to recognize correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease. Subsequently, in response to receiving an indication of a given patient's symptoms, the system determines a likelihood of the patient suffering from the epidemiological disease at least partially based upon the determined correlations.

As described hereinabove, for some applications, the epidemic-monitoring system accesses patient-population historical medical data relating to all patients within the system or relating to a subset of patients. For some such applications, the system determines the likelihood that a given patient will become infected with the epidemiological disease and/or a likelihood of the given patient developing severe symptoms at least partially based upon a combination of the patients' historical medical data and the patient-population historical medical data. For some applications, the system uses artificial-intelligence algorithms. Typically, during a machine-learning stage, the algorithm is trained to recognize correlations between historical and/or chronic medical conditions and a likelihood of becoming infected and/or a likelihood of developing severe symptoms, by analyzing patient-population historical medical data. Subsequently, in response to receiving an indication of a given patient's historical medical data, the system determines a likelihood of the patient becoming infected and/or developing severe symptoms based upon the correlations.

Reference is now made to Fig. 4A, which is a schematic illustration of a first step in a location-related risk assessment algorithm, as it appears on a patient interface, in accordance with some applications of the present invention. Reference is also made to Fig. 4B, which is an example of a heatmap that shows risk of exposure in a vicinity of the patient, in accordance with some applications of the present invention.

As described hereinabove, for some applications, epidemic -monitoring system 20 builds an epidemiological map indicating a prevalence of the epidemic at respective areas. Typically, this map is updated and monitored at health-authority interface 28 (shown in Fig. 1). For some applications, this information is used to monitor and predict community-level risk, and the path and pattern of spread (e.g., to predict and create an epidemic spread model). As described hereinabove, typically, such information is used to guide recommendations to members of the public within respective areas, by the health authority. For example, in areas that are at high risk, the health authority may issue instructions that all members of the public must remain under quarantine, whereas in areas that are at lower risk, they may advise only at-risk members of the population to remain indoors, and in areas that are at very low risk, they may allow all regular activities to continue.

For some applications, when a patient initially interacts with the system using a patient interface, the system cross-checks the patient's current and/or historic locations (which may be stored in the patient's smartphone, for example) with locations that are known to have or to have had high exposure, and/or against specific locations and times at which positively- identified patients and/or high-risk patients are known to have been present. For some applications, in response to cross-checking the patient's locations, the system indicates that the patient has had no known exposure to the infection, or outputs a recommendation and/or instruction, such as a requirement to enter quarantine for a given period of time. For some applications, in response to identifying a high-risk patient or a positively-identified patient, the health authority's epidemiological map is automatically updated to reflect this. Reference is now made to Fig. 5, which shows a table indicating trends, on a location- by-location basis (e.g., a city-by-city basis, as shown), that is generated in accordance with some applications of the present invention. For some applications, based upon location- related data, the epidemic-monitoring system generates a table (or presents data in a different format) to indicate current trends on a location-by-location basis (e.g., a city-by-city basis, as shown). Typically, such data are displayed at the health-authority interface 28 (as shown), in order to facilitate future planning by the health authority, and/or in order to help the health authority issue location- specific guidelines, recommendations, and/or directives. Typically, health-authority interface 28 comprises a program or an application that is run on an electronic device that includes a user interface and a computer processor, such as a computer, a laptop, and/or a different electronic device that is used within the health authority.

For some applications, for a given patient, the epidemic-monitoring system determines a likelihood (e.g., a probability) that the patient will contract the disease within a given time period. Alternatively or additionally, the epidemic-monitoring system is configured to determine a likelihood (e.g., a probability) that the patient will have severe outcomes as a result of becoming infected, e.g., due to age, pre-existing medical conditions, and/or location. For some applications, the determination of the aforementioned probabilities is based upon patients' responses to the questionnaires. For example, patients' responses may be analyzed using artificial-intelligence algorithms (e.g., neural networks), and/or using protocols authorized by a relevant authority. For some applications, the determinations of the aforementioned probabilities is additionally based upon location-related data relating to respective patients, such as the predicted rate of spread of the epidemic in the vicinity of the patient (e.g., within the patient's city, or other geographic area).

For some applications, based upon questionnaires that are completed by patients (and/or additional data, as described herein), the epidemic-monitoring system is configured to predict a probability of patients within respective geographical locations (e.g., within respective cities) contracting the disease at a given time in the future. For example, Fig. 6 shows a set of curves, each of which represents, for a respective city, a plot of a probability of a patient contracting the disease against a number of days into the future. Typically, these probabilities are used as weightings for predicting the probability of a given patient within a given city contracting the disease, in combination with other patient-related data.

For some applications, in order to forecast cases at a given location, one or more of the following parameters are taken into account by the epidemic-monitoring system: aggregated patient questionnaires, geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients at the location, trends in numbers of infected patients at the location, economic profile of the location, population density, weather (e.g., temperature, rainfall, humidity, sunshine, etc.) at the location, social media symptomatic data, internet searches in the location. For some applications, such data are analyzed using artificial-intelligence algorithms (e.g., neural networks), and/or using protocols authorized by a relevant authority.

The data shown in Fig. 6 are based upon real data that was collected for respective cities in Israel during the outbreak of COVID-19 in March 2020. As shown, the data indicate that on day 9, there will be an outbreak in one city (Haifa), on days 4 and 11, there will be breakouts in another city (Nesher), and on day 6, there will be a breakout in yet another city (Arad). The data also indicate that in some cities (Afula, Haifa, and Ashdod), there is a gradually increasing probability, whereas in other cities (Jerusalem, and Tel Aviv) there is a gradually decreasing probability. For one city (Bat Yam), the probability initially rises, before undergoing a steady decrease.

Reference is now made to Fig. 7, which shows a predicted heatmap indicating numbers of predicted cases at a future time, in accordance with some applications of the present invention. For some applications, based upon the location-by-location predictions (e.g., city-by-city predictions), the epidemic -monitoring system generates a predicted future heatmap, indicating the predicted spread of the epidemic at different locations at a given time in the future. Typically, such a heatmap is displayed at health-authority interface 28, in order to facilitate future planning by the health authority, and/or in order to help the health authority issue location- specific guidelines, recommendations, and/or directives. For some applications, the heatmap is animated. For example, the system may allow a user to scroll through different times in the future, and observe how the heatmap changes over time.

Reference is now made to Fig. 8, which is a schematic illustration of a forecast for the spread of an epidemic by location that is performed by epidemic-monitoring system 20, in accordance with some applications of the present invention. Typically, such a forecast is displayed at health-authority interface 28, in order to facilitate future planning by the health authority, and/or in order to help the health authority issue location-specific guidelines, recommendations, and/or directives. As described hereinabove, for some applications, epidemic-monitoring system 20 builds an epidemiological map indicating a prevalence of the epidemic at respective areas. For some applications, this information is used to monitor and predict community-level risk, and the path and pattern of spread (e.g., to predict and create an epidemic spread model). For some applications, a forecasting model is used to predict the spread of the epidemic within respective areas. For example, respective plots in Fig. 8 shows the forecast spread of COVID-19 in respective cities in Israel. For some applications, a vector autoregression algorithm (or a similar forecasting algorithm) is used to predict the spread of the epidemic within respective areas based upon data regarding the spread of the epidemic within each area to date. For some applications, the forecasting models include a spread of forecasts based upon a standard deviation of the data, as shown.

Reference is now made to Fig. 9, which is an example of a table showing a predicted number of additional cases at a given time in the future, and a percentage change in the number of cases, on a location-by-location basis (e.g., a city-by-city basis), that is generated in accordance with some applications of the present invention. For some applications, such a table is generated (or predicted data are displayed in a different format), based upon any one of the prediction and/or forecasting algorithms described herein. Typically, such a table is displayed at health-authority interface 28, in order to facilitate future planning by the health authority, and/or in order to help the health authority issue location-specific guidelines, recommendations, and/or directives.

Applications of the invention described herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non-transitory computer-readable medium) providing program code for use by or in connection with a computer or any instruction execution system, such as computer processor. For the purpose of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Typically, the computer-usable or computer readable medium is a non-transitory computer-usable or computer readable medium.

Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments of the invention.

Network adapters may be coupled to the processor to enable the processor to become coupled to other processors or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

Computer program code for carrying out operations 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.

It will be understood that the algorithms described herein, 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 algorithms described in the present application. These computer program instructions may also be stored in a computer-readable medium (e.g., a non- transitory computer-readable medium) that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the algorithms. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus 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 algorithms described in the present application. The computer processor is typically a hardware device programmed with computer program instructions to produce a special purpose computer. For example, when programmed to perform the algorithms described with reference to the Figures, the computer processor typically acts as a special purpose epidemic-monitoring computer processor. Typically, the operations described herein that are performed by a computer processor transform the physical state of a memory, which is a real physical article, to have a different magnetic polarity, electrical charge, or the like depending on the technology of the memory that is used. For some applications, operations that are described as being performed by a computer processor are performed by a plurality of computer processors in combination with each other. It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof that are not in the prior art, which would occur to persons skilled in the art upon reading the foregoing description.

Claims

1. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from patients, via patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease, on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

2. The method according to claim 1, wherein driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future comprises driving the epidemic-monitoring system to run a vector autoregression algorithm to predict the spread of the epidemiological disease at respective locations based upon data regarding the spread of the epidemiological disease at respective locations to date.

3. The method according to claim 1, wherein driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future comprises driving the epidemic -monitoring system to account for a given patient's locations over a time period over which the patient is suspected of having been infected with the epidemiological disease.

4. The method according to claim 1, wherein driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future comprises driving the epidemic-monitoring system to account for one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

5. The method according to claim 1, wherein driving the epidemic-monitoring system to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future comprises driving the epidemic -monitoring system to account for one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

6. The method according to any one of claims 1-5, wherein driving the epidemic monitoring system to generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations at the given time in the future comprises driving the epidemic-monitoring system to generate a predicted future heatmap indicating the predicted spread of the epidemiological disease at different locations at a given time in the future.

7. The method according to claim 6, wherein driving the epidemic-monitoring system to generate the predicted future heatmap comprises driving the epidemic-monitoring system to generate a predicted future heatmap that allows a user to scroll through different times in the future, and observe how the heatmap changes over time.

8. The method according to any one of claims 1-5, further comprising driving the epidemic-monitoring system to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast.

9. The method according to claim 8, wherein driving the epidemic-monitoring system to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast comprises driving the epidemic -monitoring system to cross check locations at which the given patient was known to have been present against the forecasts for the locations.

10. The method according to claim 8, further comprising driving the epidemic -monitoring system to generate an alert that the given patient should undergo a diagnostic test, based upon the predicted probability.

11. The method according to claim 8, further comprising driving the epidemic -monitoring system to generate an alert that the given patient should enter quarantine, based upon the predicted probability.

12. The method according to claim 8, further comprising driving the epidemic -monitoring system to generate an alert that family members of the given patient should enter quarantine, based upon the predicted probability.

13. The method according to claim 8, further comprising driving the epidemic -monitoring system to generate an alert that people who came into proximity with the given patient should enter quarantine, based upon the predicted probability.

14. Apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus comprising: an epidemic-monitoring system comprising one or more computer processors that are configured to: receive data from patients, via the patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease, on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

15. The apparatus according to claim 14, wherein the epidemic-monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by running a vector autoregression algorithm to predict the spread of the epidemiological disease at respective locations based upon data regarding the spread of the epidemiological disease at respective locations to date.

16. The apparatus according to claim 14, wherein the epidemic-monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for a given patient's locations over a time period over which the patient is suspected of having been infected with the epidemiological disease.

17. The apparatus according to claim 14, wherein the epidemic-monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

18. The apparatus according to claim 14, wherein the epidemic-monitoring system is configured to forecast cases of patients suffering from the epidemiological disease at respective locations at a given time in the future by accounting for one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

19. The apparatus according to any one of claims 14-18, wherein the epidemic-monitoring system is configured to generate a predicted future heatmap indicating the predicted spread of the epidemiological disease at different locations at a given time in the future.

20. The apparatus according to claim 19, wherein the epidemic-monitoring system is configured to generate a predicted future heatmap that allows a user to scroll through different times in the future, and observe how the heatmap changes over time.

21. The apparatus according to any one of claims 14-18, wherein the epidemic-monitoring system is configured to predict a probability of a given patient contracting the epidemiological disease at least partially based upon the forecast.

22. The apparatus according to claim 21, wherein the epidemic-monitoring system is configured to predict a probability of the given patient contracting the epidemiological disease by cross-checking locations at which the given patient was known to have been present against the forecasts for the locations.

23. The apparatus according to claim 21, wherein the epidemic-monitoring system is configured to generate an alert that the given patient should undergo a diagnostic test, based upon the predicted probability.

24. The apparatus according to claim 21, wherein the epidemic-monitoring system is configured to generate an alert that the given patient should enter quarantine, based upon the predicted probability.

25. The apparatus according to claim 21, wherein the epidemic-monitoring system is configured to generate an alert that family members of the given patient should enter quarantine, based upon the predicted probability.

26. The apparatus according to claim 21, wherein the epidemic-monitoring system is configured to generate an alert that people who were in proximity to the given patient should enter quarantine, based upon the predicted probability.

27. A computer software product for monitoring an epidemiological disease and for use with patient-interface device and comprising a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic -monitoring system to: receive data from patients, via the patient-interface devices; based upon the received data: analyze data to determine current trends of the epidemiological disease , on a location-by-location basis; at least partially in response thereto, forecast cases of patients suffering from the epidemiological disease at respective locations, at a given time in the future; and generate an output indicating the forecast of patients suffering from the epidemiological disease at respective locations, at the given time in the future.

28. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from a plurality of patients, via patient-interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability.

29. The method according to claim 28, wherein driving the epidemic -monitoring system to predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information comprises driving the epidemic-monitoring system to predict a probability of the given patient contracting the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

30. The method according to claim 28, wherein driving the epidemic -monitoring system to predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information comprises driving the epidemic-monitoring system to predict a probability of the given patient contracting the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

31. The method according to any one of claims 28-30, wherein driving the epidemic monitoring system to generate an output based upon the predicted probability comprises driving the epidemic -monitoring system to generate an alert that the patient should undergo a diagnostic test, based upon the predicted probability.

32. The method according to any one of claims 28-30, wherein driving the epidemic monitoring system to generate an output based upon the predicted probability comprises driving the epidemic-monitoring system to generate an alert that the patient should enter quarantine, based upon the predicted probability.

33. The method according to any one of claims 28-30, wherein driving the epidemic monitoring system to generate an output based upon the predicted probability comprises driving the epidemic-monitoring system to generate an alert that family members of the patient should enter quarantine, based upon the predicted probability.

34. The method according to any one of claims 28-30, wherein driving the epidemic monitoring system to generate an output based upon the predicted possibility comprises driving the epidemic-monitoring system to generate an alert that people who were in proximity to the patient should enter quarantine, based upon the predicted probability.

35. Apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus comprising: an epidemic-monitoring system comprising one or more computer processors that are configured to: receive data from patients, via the patient- interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability.

36. The apparatus according to claim 35, wherein the epidemic-monitoring system is configured to predict the probability of the given patient contracting or having contracted the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: geographic proximity to infected locations, proximity to infected patients' travel routes, numbers of infected patients in respective locations, and trends in numbers of infected patients at respective locations.

37. The apparatus according to claim 35, wherein the epidemic-monitoring system is configured to predict the probability of a given patient contracting or having contracted the epidemiological disease by cross-checking the patient's location-related information against one or more of the following parameters: economic profile of respective locations, population density at respective locations, weather at respective locations, social media symptomatic data, and internet searches in respective locations.

38. The apparatus according to any one of claims 35-37, wherein the epidemic-monitoring system is configured to generate an alert that the patient should undergo a diagnostic test, based upon the predicted probability.

39. The apparatus according to any one of claims 35-37, wherein the epidemic-monitoring system is configured to generate an alert that the patient should enter quarantine, based upon the predicted probability.

40. The apparatus according to any one of claims 35-37, wherein the epidemic-monitoring system is configured to generate an alert that family members of the patient should enter quarantine, based upon the predicted probability.

41. The apparatus according to any one of claims 35-37, wherein the epidemic-monitoring system is configured to generate an alert that people who were in proximity to the patient should enter quarantine, based upon the predicted probability.

42. A computer software product for use with patient-interface devices and comprising a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients, via the patient- interface devices, the data including location-related information relating to locations at which respective patients have been present; predict a probability of a given patient contracting or having contracted the epidemiological disease at least partially based upon the location-related information; and generate an output based upon the predicted probability.

43. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient- interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

44. Apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus comprising: an epidemic-monitoring system comprising one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient- interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

45. A computer software product for monitoring an epidemiological disease and for use with patient-interface devices and comprising a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via the patient- interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, determine a likelihood that each of the patients is infected with the epidemiological disease.

46. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient- interface devices; receive an indication of patients that were determined to be suffering from the epidemiological disease using diagnostic tests; in response thereto, determining correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease; and subsequently, determine a likelihood of a given patient suffering from the epidemiological disease at least partially based upon the determined correlations.

47. Apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus comprising: an epidemic-monitoring system comprising one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient- interface devices; receive an indication of patients that were determined to be suffering from the epidemiological disease using diagnostic tests; in response thereto, determine correlations between respective symptoms and a likelihood of the symptom being indicative of the epidemiological disease; and subsequently determine a likelihood of a given patient suffering from the disease at least partially based upon the determined correlations.

48. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

49. Apparatus for monitoring an epidemiological disease and for use with patient- interface devices, the apparatus comprising: an epidemic-monitoring system comprising one or more computer processors that are configured to: receive data from patients relating to current symptoms, via the patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

50. A computer software product for monitoring an epidemiological disease and for use with patient-interface devices and comprising a non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a computer cause the computer to perform the steps of: driving an epidemic-monitoring system to: receive data from patients relating to current symptoms, via the patient interface devices; access historical medical data relating to the patients; and based upon a combination of the received current symptoms and the historical medical data, generate an output to a subset of patients indicating that they should undergo a diagnostic test for the epidemiological disease.

51. A method for monitoring an epidemiological disease comprising: driving an epidemic-monitoring system to: receive data from patients and potential patients, via patient interface devices; based upon the received data: output recommendations to patients and potential patients, via the patient- interface devices; triage patients and potential patients, via a triage-center interface; and generate an epidemiological map indicating a spread of the epidemiological disease.