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. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data.


RESULTS
Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation.


CONCLUSIONS
This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Anna L
AL
Buczak
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Benjamin
B
Baugher
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Steven M
SM
Babin
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Liane C
LC
Ramac-Thomas
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Erhan
E
Guven
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Yevgeniy
Y
Elbert
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Phillip T
PT
Koshute
Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
John Mark S
JM
Velasco
National Epidemiology Center, Department of Health, Manila, Philippines.
Vito G
VG
Roque
National Epidemiology Center, Department of Health, Manila, Philippines.
Enrique A
EA
Tayag
Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand.
In-Kyu
IK
Yoon
Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland, United States of America.
Sheri H
SH
Lewis
D055907:Climatic Processes; D003715:Dengue / Q000453:epidemiology*; D004812:Epidemiologic Methods; D005544:Forecasting; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D010679:Philippines / Q000453:epidemiology; D012959:Socioeconomic Factors