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

TL;DR

This paper introduces FINDER, a machine learning model that uses web search and location data to detect foodborne illness outbreaks in real-time, significantly improving inspection accuracy and revealing complex epidemiological patterns.

Contribution

The study presents a novel real-time foodborne illness detection system using aggregated digital data, enhancing inspection precision and epidemiological understanding.

Findings

FINDER increases the likelihood of identifying unsafe restaurants by 3.1 times.

It can detect cases where the source restaurant was not the last visited.

The system reliably identifies restaurants with active food safety lapses.

Abstract

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.