Germ Tracker Monitors Spread of Sickness Using Tweets & GPS

Germ Tracker Web App Output

Germ Tracker Web App Courtesy of University of Rochester and

Claire Porter, Technology Editor for reports on a web app called Germ Tracker that pinpoints where the sick people are in public so you can avoid them and stay well.

Created by scientists at the University of Rochester, Germ Tracker analyzes thousands of tweets against pollution sources, public transportation stations, gyms, restaurants, and other GPS-based data.

Most notably, those who tweeted about going to the gym but rarely made it there got sick significantly more often.

According to lead researcher, Adam Sadilek, individuals can use this information to take charge of their health by steering clear of subway stations where many sick people may congregate.

Sadilek sees Germ Tracker assisting local authorities to better handle responses to outbursts of the flu.

Germ Tracker uses machine learning and natural language processing to distinguish between colloquial uses of “sick” like “cool!” versus “I’m really ill.”

As the app mines more tweets, it learns and gets better at predicting whether a Twitter user is indeed sick. Its classifications include not sick, low risk, sick, and high risk.

The algorithm reveals the tweet that led to the classification and allows users to override it.

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