Spatiotemporal dynamics of <i>Bacillus anthracis</i> under climate change: a machine learning approach.

ABSTRACT: This study examines the spatiotemporal dynamics of Bacillus anthracis, the causative agent of anthrax, under climate change scenarios using advanced machine learning techniques. Climate change is increasingly recognized as a critical factor influencing the distribution and transmission dynamics of infectious diseases, particularly those reliant on environmental reservoirs. Our research employs Maximum Entropy (Maxent) modeling to forecast the current global distribution of B. anthracis based on climatic factors and to predict future habitat suitability under various Coupled Model Intercomparison Project Phase 5 (CMIP5) scenarios (RCP-2.6 and RCP-8.5) for the 2050's and 2070's. We identify high-risk areas where climate change may enhance the suitability for B. anthracis

SUBMITTER: Khalaf SMH 

PROVIDER: S-EPMC12558879 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature