Prediction of high incidence of dengue in the Philippines.

Anna L Buczak,In-Kyu Yoon,Steven M Babin,Phillip T Koshute,Liane C Ramac-Thomas,John Mark S Velasco,Vito G Roque,Erhan Guven,Yevgeniy Elbert,Benjamin Baugher,Enrique A Tayag,Sheri H Lewis,Marilia Sá Carvalho

doi:10.1371/journal.pntd.0002771

Abstract

BackgroundAccurate 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.MethodsModel 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.Principal FindingsModel 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.ConclusionsThis 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.

Highlights

Dengue fever is a common human viral disease transmitted via the bite of infected Aedes mosquitoes, typically Aedes aegypti
The results reported below are based only on the performance of the models in predicting the 2011 incidence data that were not used for model development
In addition to the results for 40 provinces with good data reporting, the results for all 81 provinces are provided in order to determine how well the model can generalize to provinces that were never used in model development

Summary

Introduction

Dengue fever is a common human viral disease transmitted via the bite of infected Aedes mosquitoes, typically Aedes aegypti. These mosquitoes are capable of breeding in uncovered containers holding rain water, such as tires, buckets, flower pots, etc., that are commonly found in urban areas in the tropics [1]. Dengue incidence has increased 30-fold over the last 50 years, is endemic in more than 100 countries, and causes an estimated 50 million infections annually [2]. A severe presentation, known as dengue hemorrhagic fever (DHF) occurs primarily in patients who are reinfected with a different serotype [7].

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