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Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

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  • Kraisak Kesorn
  • Phatsavee Ongruk
  • Jakkrawarn Chompoosri
  • Atchara Phumee
  • Usavadee Thavara
  • Apiwat Tawatsin
  • Padet Siriyasatien

Abstract

Background: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings: Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions: The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).

Suggested Citation

  • Kraisak Kesorn & Phatsavee Ongruk & Jakkrawarn Chompoosri & Atchara Phumee & Usavadee Thavara & Apiwat Tawatsin & Padet Siriyasatien, 2015. "Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
  • Handle: RePEc:plo:pone00:0125049
    DOI: 10.1371/journal.pone.0125049
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    References listed on IDEAS

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    1. Michael A Johansson & Derek A T Cummings & Gregory E Glass, 2009. "Multiyear Climate Variability and Dengue—El Niño Southern Oscillation, Weather, and Dengue Incidence in Puerto Rico, Mexico, and Thailand: A Longitudinal Data Analysis," PLOS Medicine, Public Library of Science, vol. 6(11), pages 1-9, November.
    2. Elodie Descloux & Morgan Mangeas & Christophe Eugène Menkes & Matthieu Lengaigne & Anne Leroy & Temaui Tehei & Laurent Guillaumot & Magali Teurlai & Ann-Claire Gourinat & Justus Benzler & Anne Pfannst, 2012. "Climate-Based Models for Understanding and Forecasting Dengue Epidemics," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(2), pages 1-19, February.
    3. Vanessa Racloz & Rebecca Ramsey & Shilu Tong & Wenbiao Hu, 2012. "Surveillance of Dengue Fever Virus: A Review of Epidemiological Models and Early Warning Systems," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 6(5), pages 1-9, May.
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    1. Pi Guo & Tao Liu & Qin Zhang & Li Wang & Jianpeng Xiao & Qingying Zhang & Ganfeng Luo & Zhihao Li & Jianfeng He & Yonghui Zhang & Wenjun Ma, 2017. "Developing a dengue forecast model using machine learning: A case study in China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(10), pages 1-22, October.
    2. Luana Nice da Silva Oliveira & Alexander Itria & Erika Coutinho Lima, 2019. "Cost of illness and program of dengue: A systematic review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-15, February.

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