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Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

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  • Leslie Chandrakantha

    (Department of Mathematics and Computer Science, John Jay College of City University of New York, New York, NY 10019, USA)

Abstract

Dengue fever is a mosquito-borne viral disease prevalent in more than one hundred tropical and subtropical countries. Annually, an estimated 390 million infections occur worldwide. It is transmitted by the bite of an Aedes mosquito infected with the virus. It has become a major public health challenge in recent years for many countries, including Sri Lanka. It is known that climate factors such as rainfall, temperature, and relative humidity influence the generation of mosquito offspring, thus increasing dengue incidences. Identifying the climate factors that affect the spread of dengue fever would be helpful in order for the relevant authorities to take necessary actions. The objective of this study is to build a model for predicting the likelihood of having high dengue incidences based on climate factors. A logistic regression approach was utilized for model formulation. This study found a significant association between high numbers of dengue incidences and rainfall. Furthermore, it was observed that the influence of rainfall on dengue incidences was expected to be visible after some lag period.

Suggested Citation

  • Leslie Chandrakantha, 2019. "Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach," Stats, MDPI, vol. 2(2), pages 1-12, May.
    • Handle: RePEc:gam:jstats:v:2:y:2019:i:2:p:21-283:d:230204
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    References listed on IDEAS

    as
    1. Cordeiro, Gauss M. & Simas, Alexandre B., 2009. "The distribution of Pearson residuals in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3397-3411, July.
    2. Kensuke Goto & Balachandran Kumarendran & Sachith Mettananda & Deepa Gunasekara & Yoshito Fujii & Satoshi Kaneko, 2013. "Analysis of Effects of Meteorological Factors on Dengue Incidence in Sri Lanka Using Time Series Data," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
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