Evaluating Twitter for Foodborne Illness Outbreak Detection in New York City

• Published in: Online journal of public health informatics 2018-05, Vol.10 (1)
• Main Authors: Devinney, Katelynn, Bekbay, Adile, Effland, Thomas, Gravano, Luis, Howell, David, Hsu, Daniel, O'Hallorhan, Daniel, Reddy, Vasudha, Stavinsky, Faina, Waechter, HaeNa, Gutelius, Bruce
• Format: Article
• Language: English
• Subjects: ISDS 2018 Conference Abstracts
• ISSN: 1947-2579; 1947-2579
• DOI: 10.5210/ojphi.v10i1.8894

ObjectiveTo incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.IntroductionAn estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.MethodsDOHMH epidemiologists continue to collaborate with computer scientists at Columbia University who developed a text mining algorithm that identifies tweets indicating foodborne illness. Twitter data are received via a targeted application program interface query that searches for foodborne illness key words and uses metadata to select for tweets with a possible NYC location. Each tweet is assigned a sick score between 0–1; those meeting a threshold value of 0.5 are manually reviewed by an epidemiologist, and a survey link is tweeted to users who have tweeted about foodborne illness, requesting more information regarding the date and time of the foodborne illness event, restaurant details, and user contact information. Survey data are used to validate complaints and are incorporated in a daily analysis using all sources