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

arXiv: https://arxiv.org/abs/1812.01813 · HTML: https://mlsanity.com/paper/1812.01813/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection](https://mlsanity.com/paper/2312.01225/) (2023)
- [Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study](https://mlsanity.com/paper/2102.08755/) (2021)
- [Fair Decision-Making for Food Inspections](https://mlsanity.com/paper/2108.05523/) (2021)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification](https://mlsanity.com/paper/2212.13898/) (2022)
- [Continuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals](https://mlsanity.com/paper/2610.15191/) (2026)

## Abstract (arXiv)

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.