Hub
Information on evening class schedule for Tuesday

APL scientists use data to predict disease outbreaks weeks in advance

Model predicted dengue fever in pilot tests in Peru, Phillipines

Hub staff report / March 21, 2013 10:59:00 am Posted in Health, Science+Technology Tagged public health, epidemiology, big data, data modeling, applied physics laboratory

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

Related Articles

Using Twitter to track the flu

January 24, 2013 4:24:00 pm
  |   Video
Researchers find a better way to screen the tweets

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.

Read more from Applied Physics Laboratory

Editor’s note: We welcome your comments; all we ask is that you keep it civil and on-topic, and don't break any laws. We reserve the right to remove any inappropriate comments.

comments powered by Disqus