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Application US20190148023
Published 2019-05-16
Machine-learned Epidemiology
The present disclosure provides systems and methods that leverage machine-learned models in conjunction with online data to monitor and detect the spread of a disease, such as, for example, a communicable illness. In one example, a computing system can include or otherwise leverage a machine-learned disease detection model. The computing system can input search engine data and, optionally, location data respectively associated with a first plurality of users into the machine-learned disease detection model. The computing system can receive identification of a second plurality of users predicted to have the disease as an output of the machine-learned disease detection model. The second plurality of users can be a subset of the first plurality of users. The computing system can identify one or more locations associated with elevated levels of the disease based at least in part on the location data respectively associated with at least the second plurality of users.
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- 1. A computing system, comprising:
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.
- 15. A computer-implemented method to identify locations associated with a disease, the method comprising:
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.
- 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:
obtaining location history data respectively associated with a first plurality of users; inputting the location history 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 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:
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.
