Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition
Autor(es): Chen, Chia-Chern; Chang, Hsien-Chang
Resumo: 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. 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. 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. 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. (C) 2013 Published by Elsevier Ltd on behalf of The British Infection Association.
Palavras-Chave: Dengue; Disease outbreaks; Environmental monitoring; Entropy; Pattern recognition; Prevention & control; Infection; Aedes
Imprenta: Journal of Infection, v. 67, n. 1, p. 65-71, 2013
Identificador do objeto digital: 10.1016/j.jinf.2013.03.012
Descritores: Aedes aegypti - Dengue
Data de publicação: 2013
