title: 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.
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.
  
  
  RESULTS
  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).
authors.affiliation: Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand.
authors.forename: Kraisak
authors.initials: K
authors.lastname: Kesorn
authors.affiliation: Computer Science and Information Technology Department, Faculty of Science, Naresuan University, Phitsanulok, Thailand.
authors.forename: Phatsavee
authors.initials: P
authors.lastname: Ongruk
authors.affiliation: National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
authors.forename: Jakkrawarn
authors.initials: J
authors.lastname: Chompoosri
authors.affiliation: Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
authors.forename: Atchara
authors.initials: A
authors.lastname: Phumee
authors.affiliation: National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
authors.forename: Usavadee
authors.initials: U
authors.lastname: Thavara
authors.affiliation: National Institute of Health, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand.
authors.forename: Apiwat
authors.initials: A
authors.lastname: Tawatsin
authors.affiliation: 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.
authors.forename: Padet
authors.initials: P
authors.lastname: Siriyasatien
mesh_terms: D000330:Aedes / Q000821:virology*; D000465:Algorithms; D000818:Animals; D002980:Climate; D003716:Dengue Virus; D004777:Environment; D005260:Female; D006801:Humans; D015994:Incidence; D007303:Insect Vectors / Q000821:virology*; D008297:Male; D008962:Models, Theoretical; D009017:Morbidity; D015203:Reproducibility of Results; D019595:Severe Dengue / Q000453:epidemiology* / Q000635:transmission*; D060388:Support Vector Machine; D013785:Thailand / Q000453:epidemiology