ORIGINAL RESEARCH article
Front. Microbiol.
Sec. Systems Microbiology
Volume 16 - 2025 | doi: 10.3389/fmicb.2025.1659876
This article is part of the Research TopicArtificial Intelligence and mNGS in Pathogenic Microorganism ResearchView all 7 articles
Spatiotemporal Dynamics of Bacillus anthracis Under Climate Change: A Machine Learning Approach
Provisionally accepted
- 1October University for Modern Sciences and Arts, 6th of October City, Egypt
- 2King Khalid University College of Science, Abha, Saudi Arabia
- 3Al-Azhar University Faculty of Pharmacy for Girls, Nasr City, Egypt
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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 2050s and 2070s. We identify high-risk areas where climate change may enhance the suitability for B. anthracis , emphasizing the need for proactive monitoring and early-warning systems. The findings indicate potential shifts in anthrax-endemic zones, with new regions becoming conducive to the establishment of B. anthracis due to the changing climate. Our results demonstrate the applicability of machine learning in predicting disease risk, providing a framework for public health preparedness in light of evolving environmental challenges. These insights are critical for developing targeted surveillance strategies and mitigating the introduction of zoonotic diseases in a warming environment.
Keywords: Bacillus anthracis, Species distribution modeling, Climate Change, ecological niche, Epidemiology
Received: 07 Jul 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Khalaf, Alqahtani, Selim, Elsayed and Bendary. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sameh M.H. Khalaf, samhisham@msa.edu.eg
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