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Susceptible\u2010Infected\u2010Recovered (SIR) models provide useful information for understanding transmission dynamics and epidemic curves of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability parameters derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.769) and Nigeria (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u00a0=\u00a00.756), that incorporate data for temperature, precipitation, and drought index and have been calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models can be used for reasonably accurate retrospective analyses at both country\u2010wide scale for which they were calibrated and modified for smaller spatial extent, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic transmission will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools.\n                  <\/jats:p>","DOI":"10.1029\/2025gh001437","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T11:41:58Z","timestamp":1775043718000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of a Novel Climate\u2010Driven SIR Model for Cholera Prediction"],"prefix":"10.1029","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9953-6651","authenticated-orcid":false,"given":"Bailey","family":"Magers","sequence":"first","affiliation":[{"name":"Department of Environmental Engineering Sciences Geohealth and Hydrology Laboratory University of Florida  Gainesville FL USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3234-3337","authenticated-orcid":false,"given":"Kyle D.","family":"Brumfield","sequence":"additional","affiliation":[{"name":"Maryland Pathogen Research Institute University of Maryland  College Park MD USA"},{"name":"University of Maryland Institute for Advanced Computer Studies University of Maryland  College Park MD USA"},{"name":"Department of Cell Biology and Molecular Genetics University of Maryland  College Park MD USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5432-1502","authenticated-orcid":false,"given":"Rita R.","family":"Colwell","sequence":"additional","affiliation":[{"name":"Maryland Pathogen Research Institute University of Maryland  College Park MD USA"},{"name":"University of Maryland Institute for Advanced Computer Studies University of Maryland  College Park MD USA"},{"name":"Department of Cell Biology and Molecular Genetics University of Maryland  College Park MD USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8191-2348","authenticated-orcid":false,"given":"Antarpreet S.","family":"Jutla","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering Sciences Geohealth and Hydrology Laboratory University of Florida  Gainesville FL USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"13","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"Beguer\u00eda S. 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