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
Kesorn, Kraisak et al. (2016), 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, Dryad, Dataset, https://doi.org/10.5061/dryad.078bn
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).