DOI: 10.5061/dryad.078bn
title: Data from: 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

description: 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, and thus, they cannot enhance
 the capability of the predictive model’s learning algorithm. 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: We identified
 the study areas in three provinces (Nakhon Pathom, Ratchaburi, and Samut
 Sakhon) of central Thailand that were reported to have a high incidence of
 dengue outbreaks. Prior to being added to the model, the infection data of
 the dengue vector, Aedes aegypti, the climate parameters, and the
 population density were collected from various sources and standardized.
 This process ensured that the data were not overwhelmed by each other in
 terms of the distance measures and to enhance the model effectiveness. The
 proposed framework consisted of the following three major parts: 1) data
 integration, 2) model construction, and 3) model evaluation. We discovered
 that the Aedes 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. The support
 vector machine (SVM), a machine learning technique, has been receiving
 attention in many research areas due to its remarkable generalization
 performance. In this research, we applied the SVM with the radial basis
 function (RBF) kernel, referred to as the SVM-R, 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 Aedes aegypti female mosq uitoes
 and larvae improved the morbidity rate forecasting efficiency better than
 the climate parameters used in classical frameworks. This approach is more
 reliable and practical for monitoring dengue outbreaks, particularly in
 locations with similar climates because it does not rely on only climate
 factors. In addition, 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).
description: PONE-D-14-48810Raw data for PLOS ONE journal entitle: 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