Machine Learning

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Friday, March 9, 2018 - 7:00pm

Predicting the Future of Infectious Disease

Cary Institute disease ecologist Dr. Barbara Han will discuss how, with the help of artificial intelligence and machine learning, her team analyzes data on animals, disease, and geography to pinpoint areas at risk of future disease outbreaks.


Ebola and bats


Filoviruses have devastating effects on people and primates, as evidenced by the 2014 Ebola outbreak in West Africa. For nearly 40 years, preventing spillovers has been hampered by an inability to pinpoint which wildlife species harbor and spread the viruses.

$2M NSF grant harnesses big data & AI to advance disease prevention

Team to develop tools to map areas at risk of zoonotic disease outbreaks with new NSF grant

The Cary Institute teams with IBM Research to address Zika

When the Zika virus arrived in Brazil, it went largely unnoticed until infected infants were born with microcephaly, a neurological disorder marked by a small head caused by severe underdevelopment of brain tissue in utero. As the number of Zika-affected babies grew, the World Health Organization moved quickly to declare Zika virus a public health emergency of international concern.

Related Projects

Machine Learning to Predict Zoonotic Disease

Why do the majority of human infectious diseases originate from wildlife? Our lab seeks to identify intrinsic characteristics of wild species (e.g., life history, ecological, physiological traits) that signal their potential to be future reservoirs of zoonotic diseases (human diseases with animal origins).

Cary Institute of Ecosystem Studies | Millbrook, New York 12545 | Tel (845) 677-5343

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