APL scientists use data to predict disease outbreaks weeks in advance

Model predicted dengue fever in pilot tests in Peru, Phillipines

Image caption: The PRISM team has used a data modeling method to successfully predicted dengue fever in pilot tests in Peru and the Philippines.

Credit: APL

A team of scientists from The Johns Hopkins University Applied Physics Laboratory has developed a way of accurately predicting dengue fever outbreaks several weeks before they occur using data modeling.

The new method, known as PRISM—Predicting Infectious Disease Scalable Model—extracts relationships among clinical, meteorological, climatic, and socio-political data. It has successfully predicted dengue fever in pilot tests in Peru and the Philippines, though it can be used in any geographical region and extended to other environmentally influenced illnesses, including malaria and influenza.

PRISM, developed by APL's Anna Buczak and a team of researchers for the U.S. Department of Defense, is designed to help decision-makers and planners assess the future risk of a disease outbreak occurring in a specific area at a specific time.

"By predicting disease outbreaks when no disease is present, PRISM has the potential to save lives by allowing early public health intervention and decreasing the impact of an outbreak," says Sheri Lewis, APL's Global Disease Surveillance Program manager.

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