Exploring the Impact of Climate Variables on Livestock Anthrax Outbreaks: A Machine Learning Approach

Abstract

Anthrax, a globally significant disease, poses substantial threats to both livestock and human populations. Timely identification of anthrax outbreaks is paramount to mitigate its impact on animal health, human health, and public safety. This study aims to construct a predictive model for livestock anthrax disease occurrence. By leveraging the potential of advanced Machine-Learning techniques, we projected the likelihood of anthrax outbreaks across India, through incorporating a diverse set of meteorological, and remote sensing parameters. The ultimate goal is to establish a spatial risk map that can serve as an early warning system, aiding in the anticipation and management of future anthrax outbreaks in India's livestock population. Our analysis revealed elevated risk zones for anthrax outbreaks in the southern and north-eastern regions of India, contrasting with medium to low-risk areas in the central parts. Notably, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), rainfall, soil moisture, and wind speed emerged as pivotal variables driving the model's predictive accuracy. Among the employed models, the random forest, adaptive boosting, and classification tree analysis approaches showcased superior performance in livestock anthrax risk assessment. The risk map was generated using significant variables by exploiting best fitted models. These findings hold profound implications for policymakers, guiding the targeted deployment of control strategies against anthrax outbreaks. The dynamic risk maps generated through this study enhance public awareness, equipping decision-makers with vital insights for informed action. By spotlighting risk management endeavours, these maps further enhance governance and risk mitigation efforts.