  Machine learning has become an increasingly powerful tool for solving complex
problems, and its application in public health has been underutilized. The
objective of this study is to test the efficacy of a machine-learned model of
foodborne illness detection in a real-world setting. To this end, we built
FINDER, a machine-learned model for real-time detection of foodborne illness
using anonymous and aggregated web search and location data. We computed the
fraction of people who visited a particular restaurant and later searched for
terms indicative of food poisoning to identify potentially unsafe restaurants.
We used this information to focus restaurant inspections in two cities and
demonstrated that FINDER improves the accuracy of health inspections;
restaurants identified by FINDER are 3.1 times as likely to be deemed unsafe
during the inspection as restaurants identified by existing methods.
Additionally, FINDER enables us to ascertain previously intractable
epidemiological information, for example, in 38% of cases the restaurant
potentially causing food poisoning was not the last one visited, which may
explain the lower precision of complaint-based inspections. We found that
FINDER is able to reliably identify restaurants that have an active lapse in
food safety, allowing for implementation of corrective actions that would
prevent the potential spread of foodborne illness.
