Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar

Abstract

Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75–95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k-fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash–Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services.