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.

Journal: PloS one
PMID: 25961289

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.

Authors

  • Kraisak Kesorn
    Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand.
  • Phatsavee Ongruk
    Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand.
  • Jakkrawarn Chompoosri
    National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
  • Atchara Phumee
    Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Usavadee Thavara
    National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
  • Apiwat Tawatsin
    National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
  • Padet Siriyasatien
    Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Excellence Center for Emerging Infectious Disease, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.

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