{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:49:49Z","timestamp":1792518589493,"version":"4.5.0"},"reference-count":181,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>Microbial contamination of water sources is a pressing global challenge, disproportionately affecting developing regions with inadequate infrastructure and limited access to safe drinking water. In the Global South, waterborne pathogens such as bacteria, viruses, protozoa, and helminths contribute to diseases like cholera, dysentery, and typhoid fever, resulting in severe public health burdens. Predictive modeling emerges as a pivotal tool in addressing these challenges, offering data-driven insights to anticipate contamination events and optimize mitigation strategies. This review highlights the application of predictive modeling techniques\u2014including machine learning, hydrological simulations, and quantitative microbial risk assessment \u2014to identify contamination hotspots, forecast pathogen dynamics, and inform water resource allocation in the Global South. Predictive models enable targeted actions to improve water safety and lower the prevalence of waterborne diseases by combining environmental, socioeconomic, and climatic factors. Water resources in the Global South are increasingly vulnerability to microbial contamination, and the challenge is exacerbated by rapid urbanization, climate variability, and insufficient sanitation infrastructure. This review underscores the importance of region-specific modeling approaches. Case studies from sub-Saharan Africa and South Asia demonstrated the efficacy of predictive modeling tools in guiding public health actions connected to environmental matrices, from prioritizing water treatment efforts to implementing early-warning systems during extreme weather events. Furthermore, the review explores integrating advanced technologies, such as remote sensing and artificial intelligence, into predictive frameworks, highlighting their potential to improve accuracy and scalability in resource-constrained settings. Increased funding for data collecting, predictive modeling tools, and cross-sectoral cooperation between local communities, non-governmental organizations, and governments are all recommended in the review. Such efforts are critical for developing resilient water systems capable of withstanding environmental stressors and ensuring sustainable access to safe drinking water. By leveraging predictive modeling as a core component of water management strategies, stakeholders can address microbial contamination challenges effectively, safeguard public health, and contribute to achieving the United Nations\u2019 Sustainable Development Goals.<\/jats:p>","DOI":"10.3389\/fmicb.2025.1504829","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T12:24:01Z","timestamp":1743596641000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":48,"title":["Modeling solutions for microbial water contamination in the global south for public health protection"],"prefix":"10.3389","volume":"16","author":[{"given":"Sylvester Chibueze","family":"Izah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew Chidozie","family":"Ogwu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"137894","DOI":"10.1016\/j.scitotenv.2020.137894","article-title":"Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern.","volume":"722","author":"Abimbola","year":"2020","journal-title":"Sci. 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