Evaluating Environmental and Remote Sensing Factors in Theileriosis Risk Prediction for Bovine in Kerala, India: Navigating Post-Flood Climate Dynamics

Theileriosis, a parasitic disease, caused by Theileria spp. and transmitted through ticks, poses a significant threat to livestock, leading to elevated morbidity and mortality rates. This study investigated the incidence trend of theileriosis in Kerala, India, over three years (2019–21). Notably, the research unveiled a substantial upsurge in bovine theileriosis cases within Kerala during this period, partly attributed to the state’s severe flooding and landslides in 2018, triggered by incessant monsoon rains. The present study envisaged pinpointing the risk factors underlying the prevalence of theileriosis in Kerala. Employing linear discriminant analysis, key environmental and remote sensing variables influencing the disease’s incidence were identified. Subsequently, these risk factors underwent climate disease modelling, leading to the creation of risk maps. To predict areas sensitive to theileriosis outbreaks in Kerala, two regression models and nine machine learning models were employed. The gradient boost and random forest models demonstrated the most accurate fit among these. The study also estimated the basic reproduction number (R₀), which ranged from 0.89 to 1.8. This value indicates a high potential for Theileria spp. transmission within the study area. Consequently, the research outcomes offer valuable insights into pinpointing high risk theileriosis locations in livestock in Kerala