Objectives: The prediction of dengue outbreaks is a critical concern in many countries. However, the setup of an ideal prediction system requires establishing numerous monitoring stations and performing data analysis, which are costly, time-consuming, and may not achieve the desired results. In this study, we developed a novel method for predicting impending dengue fever outbreaks several weeks prior to their occurrence.
Methods: By reversing moving approximate entropy algorithm and pattern recognition on time series compiled from the weekly case registry of the Center for Disease Control, Taiwan, 1998-2010, we compared the efficiencies of two patterns for predicting the outbreaks of dengue fever.
Results: The sensitivity of this method is 0.68, and the specificity is 0.54 using Pattern A to make predictions. Pattern B had a sensitivity of 0.90 and a specificity of 0.46. Patterns A and B make predictions 3.1 ± 2.2 weeks and 2.9 ± 2.4 weeks before outbreaks, respectively.
Conclusions: Combined with pattern recognition, reversed moving approximate entropy algorithm on the time series built from weekly case registry is a promising tool for predicting the outbreaks of dengue fever.
Copyright © 2013. Published by Elsevier Ltd.