Machine-learned epidemiology: real-time detection of foodborne illness at scale.

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.

Predicting concentrations using limited qPCR deployments at Chicago beaches.

Culture-based methods to measure () are used by beach administrators to inform whether bacteria levels represent an elevated risk to swimmers. Since results take up to 12 h, statistical models are used to forecast bacteria levels in lieu of test results; however they underestimate days with elevated fecal indicator bacteria levels. Quantitative polymerase chain reaction (qPCR) tests return results within 3 h but are 2-5 times more expensive than culture-based methods.