Application US20190148023

Machine-Learned Epidemiology
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US20190148023

1. A computing system, comprising: (9) (3)

one or more processors;

a machine-learned disease detection model; and

one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising: obtaining search engine data and location history data respectively associated with a first plurality of users;

inputting at least the search engine data into a machine-learned disease detection model;

receiving, as an output of the machine-learned disease detection model, identification of a second plurality of users predicted to have the disease, the second plurality of users being a subset of the first plurality of users; and

identifying one or more locations associated with elevated levels of the disease based at least in part on the location history data respectively associated with at least the second plurality of users.

2. The computing system of claim 1, wherein the operations further comprise, prior to said inputting, receiving, and identifying: (0) (3)

3. The computing system of claim 1, wherein the search engine data comprises data descriptive of one or more of: (0) (5)

search queries;

scrolling actions;

result selection actions;

time spent on user-selected results; or

content of user-selected results.

4. The computing system of claim 1, wherein inputting at least the search engine data into the machine-learned disease detection model comprises inputting both the search engine data and the location history data into the machine-learned disease detection model. (0)

5. The computing system of claim 1, wherein inputting at least the search engine data into the machine-learned disease detection model comprises inputting both the search engine data and additional feature data into the machine-learned disease detection model, wherein the additional feature data comprises one or more of: (0) (5)

step count data generated by a computing device that is worn;

heart rate data generated by a computing device that is worn;

weather condition data;

mosquito density data; or

water quality data.

6. The computing system of claim 1, wherein identifying the one or more locations associated with elevated levels of the disease comprises identifying the one or more locations associated with elevated levels of the disease based at least in part on the location history data respectively associated with both the second plurality of users and a third plurality of users, the third plurality of users comprising those users included in the first plurality of users but not the second plurality of users. (1) (0)

7. The computing system of claim 6, wherein identifying the one or more locations associated with elevated levels of the disease comprises: (3) (3)

identifying a plurality of candidate locations;

determining, for each of the plurality of candidate locations, a ratio of a first number of the second plurality of users that have visited the candidate location within a threshold amount of time to a second number of the third plurality of users that have visited the candidate location within the threshold amount of time; and

identifying one or more of the candidate locations as being associated with the disease based at least in part on the respective ratios determined for the plurality of candidate locations.

8. The computing system of claim 7, wherein identifying the one or more of the candidate locations as being associated with the disease based at least in part on the respective ratios comprises comparing each respective ratio to a threshold value. (0)

9. The computing system of claim 7, wherein the disease comprises foodborne illness and the plurality of candidate locations comprises a plurality of restaurants. (0)

10. The computing system of claim 7, wherein the plurality of candidate locations comprises a plurality of geographic areas. (0)

11. The computing system of claim 1, wherein identifying the one or more locations associated with elevated levels of the disease comprises: (0) (2)

12. The computing system of claim 1, further comprising: (0) (2)

13. The computing system of claim 1, further comprising: (0) (1)

14. The computer-implemented method of claim 1, wherein the machine-learned disease detection model comprises one or more of: a support vector machine; a deep neural network; or a log linear model. (0)

15. A computer-implemented method to identify locations associated with a disease, the method comprising: (3) (4)

obtaining, by one or more computing devices, search engine data and location data respectively associated with a first plurality of users;

inputting, by the one or more computing devices, at least the search engine data into a machine-learned disease detection model;

receiving, by the one or more computing devices as an output of the machine-learned disease detection model, identification of a second plurality of users predicted to have the disease, the second plurality of users being a subset of the first plurality of users;

identifying, by the one or more computing devices, one or more locations associated with the disease based at least in part on the location data respectively associated with at least the second plurality of users.

16. The computer-implemented method of claim 15, wherein inputting, by the one or more computing devices, at least the search engine data into the machine-learned disease detection model comprises inputting, by the one or more computing devices, both the search engine data and the location data into the machine-learned disease detection model. (0)

17. The computer-implemented method of claim 15, wherein identifying, by the one or more computing devices, the one or more locations associated with the disease comprises identifying, by the one or more computing devices, the one or more locations associated with the disease based at least in part on the location data respectively associated with the second plurality of users and a third plurality of users, the third plurality of users comprising those users included in the first plurality of users but not the second plurality of users. (0)

18. The computer-implemented method of claim 15, wherein: (1) (2)

the location data comprises predicted future location data; and

identifying, by the one or more computing devices, the one or more locations associated with the disease comprises identifying, by the one or more computing devices, one or more locations predicted to be associated with the disease in the future based at least in part on the predicted future location data respectively associated with at least the second plurality of users.

19. The computer-implemented method of claim 18, further comprising: (0) (1)

20. One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising: (0) (4)

obtaining location history data respectively associated with a first plurality of users;

inputting the location history data into a machine-learned disease detection model;

identifying one or more locations associated with the disease based at least in part on the location history data respectively associated with at least the second plurality of users.

21. A computer-implemented method, the method comprising: (1) (5)

receiving, by one or more computing devices, user data;

determining, by the one or more computing devices, correlations between the user data and one or both of disease or location;

storing, by the one or more computing devices, the correlations;

iteratively updating, by the one or more computing devices, the correlations upon receipt of new user data to form a predictive disease detection model; and

using, by the one or more computing devices, the predictive disease detection model to determine a predicted causation of a disease outbreak.

22. The computer-implemented method of claim 21, wherein the predicted causation comprises a predicted location. (0)

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Application US20190148023

Machine-Learned Epidemiology

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