Systems and methods for using machine learning with epidemiological modeling

Patent metadata

Field	Value
Publication number	US-2022199266-A1
Application number	US-202117546917-A
Country	US
Kind code	A1
Filing date	Dec 9, 2021
Priority date	Dec 10, 2020
Publication date	Jun 23, 2022
Grant date	—

Abstract

Systems and methods of epidemiological modeling using machine learning are provided, and can include receiving values for an occurrence of the infectious disease during a first time period, generating, from a model trained by a machine learning system, predictions for the occurrence of the infectious disease over a second time period, performing, by a simulator using the predictions, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period, and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.

First claim

What is claimed is:

1 . A method of modeling at least one infectious disease, comprising:
receiving, from one or more data sources, data comprising values associated with an occurrence of the infectious disease during a first time period; generating, using one or more models trained by a machine learning system taking as input the data from one or more of the data sources, one or more predictions from the received data for the occurrence of the infectious disease over a second time period different from the first time period; performing, by a simulator using the one or more predictions generated by the one or more models, one or more simulations of the occurrence of the infectious disease in one or more geographic regions during one or more time periods subsequent to the second time period; and providing, to a user interface, a first simulation of the one or more simulations performed by the simulator for a first geographic region of the one or more geographic regions during a time period of the one or more time periods.

2 . The method of claim 1 , comprising:
receiving the data via a real-time data feed from at least one of the one or more data sources.

3 . The method of claim 1 , comprising:
training the machine learning system to generate at least one of the one or more models based on one or more time values associated with one or more of the values.

4 . The method of claim 1 , wherein the infectious disease comprises at least one of a communicable disease, a reportable disease, or a viral disease.

5 . The method of claim 1 , wherein the disease is selected from the group consisting of Anthrax, Arboviral diseases (diseases caused by viruses spread by mosquitoes, sandflies, ticks, etc.) such as West Nile virus, eastern and western equine encephalitis, Babesiosis, Botulism, Brucellosis, Campylobacteriosis, Chancroid, Chickenpox, Chlamydia , Cholera, Coccidioidomycosis, Coronavirus (COVID-19), Cryptosporidiosis, Cyclosporiasis, Dengue virus infections, Diphtheria, Ehrlichiosis, Foodborne disease outbreak, Giardiasis, Gonorrhea, Haemophilus influenza (invasive disease), Hantavirus pulmonary syndrome, Hemolytic uremic syndrome (post-diarrheal), Hepatitis A, Hepatitis B, Hepatitis C, HIV infection, Influenza-related infant deaths, Invasive pneumococcal disease, Lead (elevated blood level), Legionnaire disease (legionellosis), Leprosy, Leptospirosis, Listeriosis, Lyme disease, Malaria, Measles, Meningitis (meningococcal disease), Mumps, Novel influenza A virus infections, Pertussis, Pesticide-related illnesses and injuries, Plague, Poliomyelitis, Poliovirus infection (nonparalytic), Psittacosis, Q-fever, Rabies (human and animal cases), Rubella (including congenital syndrome), Salmonella paratyphi and typhi infections, Salmonellosis , Severe acute respiratory syndrome-associated coronavirus disease, Shiga toxin-producing Escherichia coli (STEC), Shigellosis, Smallpox, Syphilis (including congenital syphilis), Tetanus, Toxic shock syndrome (other than streptococcal), Trichinellosis, Tuberculosis, Tularemia, Typhoid fever, Vancomycin intermediate Staphylococcus aureus (VISA), Vancomycin resistant Staphylococcus aureus (VRSA), Vibriosis, Viral hemorrhagic fever (including Ebola virus, Lassa virus, among others), Waterborne disease outbreak, Yellow fever, and Zika virus disease and infection (including congenital).

6 . The method of claim 1 , wherein the infectious disease at least one of COVID-19, a strain corresponding to COVID-19, or a variant of SARS-CoV-2.

7 . The method of claim 1 , wherein the values indicate at least one of a number of cases of the infectious disease, a number of deaths caused by the infectious disease, testing data, vaccination rates, or hospitalization rates.

8 . The method of claim 1 , wherein the user interface comprises a dashboard application configured to interface with the simulator to generate a plurality of simulations for a plurality of geographic regions responsive to user input.

9 . The method of claim 8 , wherein the dashboard application is configured to display a hotspot predicted by the simulator for the infectious disease during the one or more time periods subsequent to the second time period.

10 . A system to model at least one infectious disease, comprising:
a machine learning model executable on one or more processors coupled to memory and configured to:
receive, from one or more data sources, data comprising values associated with an occurrence of an infectious disease during a first time period; and
generate one or more first forecasts from the received data for the occurrence of the infectious disease for a time period between the first time period and one or more time periods; and
a simulator executable on the one or more processors coupled to the memory and configured to:
generate one or more second forecasts of the occurrence of the infectious disease in one or more geographic regions for the one or more time periods; and
provide for display via a user interface a forecast of the one or more second forecasts for a first geographic region of the one or more geographic regions during at least one of the one or more time periods.

11 . The system of claim 10 , wherein a duration of the one or more first forecast is less than a duration of the one or more second forecasts.

12 . The system of claim 10 , wherein the one or more processors are further configured to perform a grid search to identify optimal parameters to feed to the simulator.

13 . The system of claim 12 , wherein the simulator is further configured to use the one or more first forecasts generated by the machine learning model and the optimal parameters to generate the one or more second forecasts.

14 . The system of claim 10 , wherein the one or more second forecasts indicate a number of deaths associated with the infectious disease based on at least one of physical distancing, lockdowns, or testing.

15 . The system of claim 10 , wherein the one or more processors are further configured to provide, based on the one or more second forecast and for display, at least one of a daily incidence level chart, a weekly incidence trend chart, an incidence level map, or a testing level chart.

16 . The system of claim 10 , wherein the simulator is further configured to:
generate a plurality of forecasts for a plurality of geographic regions; rank the plurality of geographic regions based on the plurality of forecasts; and select, based on an occurrence reduction policy, a highest ranking geographic region from the plurality of ranked geographic regions; and generate a notification to cause a reduction in an occurrence of the infectious disease in the highest ranking geographic region.

17 . The system of claim 10 , wherein:
the machine learning model comprises a time-series model configured to generate a short-term forecast up to 12 weeks from a current time using the data encoded with information associated with at least one of demographics, physical distancing policies, mobility, historical number of cases of the infectious disease, historical number of deaths of the infectious disease, or geospatial information; and the simulator is further configured to use the short-term forecast to generate a long-term forecast greater than the 12 weeks from the current time.

18 . The system of claim 17 , wherein the simulator is further configured to generate the long-term forecasts using a stochastic model combined with a mechanistic simulator, wherein the stochastic model calibrates the mechanistic simulator.

Assignees

• Datarobot Inc

Inventors

• Achin Jeremy
• Shamwell Earl Jared
• Schmidt Michael
• Heiser Mackenzie
• Farrell Patrick
• Nitzken Matt
• Bowns Jared
• Cameron Nathan
• Beairsto Adam
• Schuren Jay
• Saxena Mohak

Classifications

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• G16H40/67

  for remote operation · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H50/80 Primary

  for detecting, monitoring or modelling epidemics or pandemics, e.g. flu · CPC title