Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition

Chia Chern Chen, Hsien-Chang Chang

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)65-71
Number of pages7
JournalJournal of Infection
Volume67
Issue number1
DOIs
Publication statusPublished - 2013 Jul 1

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Dengue
Entropy
Disease Outbreaks
Registries
Centers for Disease Control and Prevention (U.S.)
Taiwan

All Science Journal Classification (ASJC) codes

  • Microbiology (medical)
  • Infectious Diseases

Cite this

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abstract = "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.",
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Predicting dengue outbreaks using approximate entropy algorithm and pattern recognition. / Chen, Chia Chern; Chang, Hsien-Chang.

In: Journal of Infection, Vol. 67, No. 1, 01.07.2013, p. 65-71.

Research output: Contribution to journalArticle

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