Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition

Chia Chern Chen, Hsien Chang Chang

研究成果: Article同行評審

13 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)65-71
頁數7
期刊Journal of Infection
67
發行號1
DOIs
出版狀態Published - 2013 七月

All Science Journal Classification (ASJC) codes

  • 微生物學(醫學)
  • 傳染性疾病

指紋

深入研究「Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition」主題。共同形成了獨特的指紋。

引用此