[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124419-en":3,"doc-seo-124419-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124419,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Spatiotemporal dynamics of Bacillus anthracis under climate change - a machine learning approach","Study of Bacillus anthracis spatiotemporal dynamics under climate change uses machine learning to assess how environmental drivers reshape distribution and transmission. Maximum Entropy (Maxent) modeling estimates current global habitat suitability from climatic variables and projects future risk under CMIP5 scenarios RCP-2.6 and RCP-8.5 for the 2050s and 2070s. High-risk areas are identified where climate change may expand suitability for anthrax, suggesting potential shifts in endemic zones and new regions becoming conducive. Results support proactive monitoring, early-warning systems, and targeted surveillance for public health preparedness against zoonotic introduction in warming environments.","TYPE Original Research PUBLISHED 14 October 2025  \nDOI 10.3389/fmicb.2025.1659876  \nOPEN ACCESS  \nEDITED BY  \nWei Wang,  \nJiangsu Institute of Parasitic Diseases (JIPD), China  \nREVIEWED BY  \nWilly A. Valdivia-Granda,  \nOrion Integrated Biosciences, United States Stephen Allen Morse,  \nIHRC, Inc., United States  \n*CORRESPONDENCE  \nSameh M. H. Khalaf  \n [samhisham@msa.edu.eg](samhisham@msa.edu.eg)  \nRECEIVED 07 July 2025  \nACCEPTED 29 September 2025  \nPUBLISHED 14 October 2025  \nCITATION  \nKhalaf SMH, Alqahtani MSM, Selim YA, Elsayed KO and Bendary HA (2025)  \nSpatiotemporal dynamics of Bacillus anthracis under climate change: a machine learning approach.  \nFront. Microbiol. 16:1659876 .  \ndoi: 10.3389/fmicb.2025.1659876  \nCOPYRIGHT  \n© 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) and the copyright owner(s) 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.  \nSpatiotemporal dynamics of Bacillus anthracis under climate change: a machine learning approach  \nSameh M. H. Khalaf  1*, Monerah S. M. Alqahtani2 ,  \nYousef A. Selim1 , Kenoz O. Elsayed1 and Hager A. Bendary3  \n1 Faculty of Biotechnology, October University for Modern Sciences and Arts (MSA University), 6th October City, Egypt, 2 Department of Biology, Faculty of Science, King Khalid University, Abha, Saudi Arabia, 3 Department of Microbiology and Immunology, Faculty of Pharmacy (Girls), Al-Azhar University, Cairo, Egypt  \nThis 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 asa 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, 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.  \nKEYWORDS  \nBacillus anthracis, species distribution modeling, climate change, ecological niche, epidemiology  \nIntroduction  \nClimate change is widely acknowledged as a significant factor influencing the distribution and transmission dynamics of infectious illnesses, especially those reliant on environmental reservoirs or vector-dependent routes (Rocklöv and Dubrow, 2020; Franklinos et al., 2019). Anthrax, caused by the spore-forming bacterium Bacillus anthracis, constitutes a considerable zoonotic risk with intricate ecological interdependencies. The spore is the infectious form and its ability to survive in soil, along with its need on particular climatic and soil conditions, renders its epidemiology acutely responsive to environmental alterations (Hugh-Jones and Blackburn, 2009; Carlson et al., 2019) . Emerging anthrax cases in regions pre","cbCaiv4zZVUfLr1u","https://ap.wps.com/l/cbCaiv4zZVUfLr1u","pdf",10731343,1,12,"English","en",105,"# Introduction\n## Climate change and infectious disease distribution\n## Ecological niche drivers of Bacillus anthracis\n## Species distribution modeling and Maxent","[{\"question\":\"What modeling method is used to study Bacillus anthracis under climate change?\",\"answer\":\"The study uses Maximum Entropy (Maxent) modeling to forecast current distribution and future habitat suitability based on climatic factors.\"},{\"question\":\"Which climate change scenarios and time horizons are considered?\",\"answer\":\"Future suitability is projected under CMIP5 scenarios RCP-2.6 and RCP-8.5 for the 2050s and 2070s.\"},{\"question\":\"What do the results suggest about anthrax endemic zones?\",\"answer\":\"The findings indicate potential shifts in anthrax-endemic zones, with some new regions becoming more conducive as climate conditions change.\"}]","Spatiotemporal dynamics of Bacillus anthracis under climate change - a machine learning approach | PDF",1785822162,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"spatiotemporal-dynamics-of-bacillus-anthracis-under-climate-change-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/spatiotemporal-dynamics-of-bacillus-anthracis-under-climate-change-a-machine-learning-approach/124419/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What modeling method is used to study Bacillus anthracis under climate change?","Question",{"text":75,"@type":76},"The study uses Maximum Entropy (Maxent) modeling to forecast current distribution and future habitat suitability based on climatic factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which climate change scenarios and time horizons are considered?",{"text":80,"@type":76},"Future suitability is projected under CMIP5 scenarios RCP-2.6 and RCP-8.5 for the 2050s and 2070s.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results suggest about anthrax endemic zones?",{"text":84,"@type":76},"The findings indicate potential shifts in anthrax-endemic zones, with some new regions becoming more conducive as climate conditions change.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]