Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

Background: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.

Methods: We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea.

Prediction of high incidence of dengue in the Philippines.

Background: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines.

Methods: Model inputs were chosen based on studies indicating variables that may impact dengue incidence.