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On July 29, 2015, the New York City Department of Health and Mental Hygiene sent out an alert — 31 people in the South Bronx had contracted Legionnaires’ disease, a lung infection from waterborne bacteria that kills about 1 out of every 10 people who get it. By the time officials found the source (a cooling tower) and contained the spread, 128 people had contracted Legionnaires’ and 12 people had died. It was the largest outbreak of Legionnaires’ disease in the city’s history — an outbreak that was first detected by a computer program.

In less than a month, the outbreak was over — the software had helped investigators narrow in on the infected cooling tower and might have saved lives. Few health departments are as advanced at fighting communicable diseases as New York’s. But this was not the first time investigators have used software and data to help fight and detect diseases. A recent special edition of the Journal of Infectious Diseases was devoted entirely to studies on how to use big data to detect and model infectious diseases. In Europe, Influenzanet allows people in 11 countries to report influenza-like symptoms, and in the U.S., the National Notifiable Diseases Surveillance System allows health departments to voluntarily share data on public health and disease as part of efforts to identify and stop outbreaks. But Sharon Greene, lead author of a new paper describing New York’s outbreak detection program and director of the Data Analysis Unit at the city’s Bureau of Communicable Disease, wanted to go beyond just software that shared data. A program that can look through that data itself and identify potential outbreaks can have a significant impact.

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Image: By Original uploaded by James.folsom (Transfered by Syp) – Own work by James.folsom, Public Domain, https://commons.wikimedia.org/w/index.php?curid=12201702

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