A New Methodology to Comprehend the Effect of El Niño and La Niña Oscillation in Early Warning of Anthrax Epidemic Among Livestock

Abstract

Anthrax is a highly fatal zoonotic disease that affects all species of livestock. The study aims to develop an early warning of epidemiological anthrax using machine learning (ML) models and to study the effect of El Niño and La Niña oscillation, as well as the climate–disease relationship concerning the spatial occurrence and outbreaks in Karnataka. The disease incidence data are divided based on El Niño and La Niña events from 2004–2019 and subjected to climate-disease modeling to understand the disease pattern over the years. Machine learning models were implemented using R statistical software version 3.1.3 with Livestock density, soil profile, and meteorological and remote sensing variables as risk factors associated with anthrax incidence. Model evaluation is performed using statistical indices, viz., Cohen’s kappa, receiver operating characteristic (ROC) curve, true skill statistics (TSS), etc. Models with good predictive power were combined to develop an average prediction model. The predicted results were mapped onto the Risk maps, and the Basic reproduction numbers (R0) for the districts that are significantly clustered were calculated. Early warning or risk prediction developed with a layer of R0 superimposed on a risk map helps in the preparedness for the disease occurrence, and precautionary measures before the spread of the disease.