Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India.

ABSTRACT: The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps wer

SUBMITTER: Suresh KP 

PROVIDER: S-EPMC10686966 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature