A team of scientists from The Johns Hopkins University Applied Physics Laboratory (APL) has developed a novel method to accurately predict dengue fever outbreaks several weeks before they occur. The new method, known as PRedicting Infectious Disease Scalable Model (PRISM), extracts relationships between clinical, meteorological, climatic, and socio-political data in Peru and in the Philippines. It can be used in any geographical region and extended to other environmentally influenced infections affecting public health and military forces worldwide. PRISM is aimed at helping decision-makers and planners assess the future risk of a disease occurring in a specific geographic area at a specific time. Developed by APL's Dr. Anna Buczak and a team of researchers for the Department of Defense (DoD), PRISM predicts the severity of a given disease at a specific time and place with quantifiable accuracy, using original analytical and statistical methods. "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 Dr. Sheri Lewis, APL's Global Disease Surveillance Program Manager. DoD is currently evaluating PRISM for use in mitigating the effects of infectious disease in various operational settings. PRISM's distinctive prediction method utilizes Fuzzy Association Rule Mining (FARM) to extract relationships between multiple variables in a data set. These relationships form rules, and when the best set of rules is automatically chosen, a classifier is formed. The classifier is then used to predict future incidence of the disease – in this case dengue fever, the second most common mosquito-borne disease, which puts more than one-third of the world's population at risk.
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