Agboka , Komi MensahNg´anga, Allan MuohiSokame, Bonoukpoe MawukoBaleba, Steve B.S.Landmann , TobiasAbdel-Rahman, Elfatih M.Tanga , Chrysantus M.Diallo, Souleymane2025-10-282025-10-282025-11Agboka, K.M., Nganga, A.M., Sokame, B.M. et al. 2025, 'Climate-driven potential for tularemia in East Africa : skill testing and ecological consistency of a transferred risk model', Spatial and Spatio-temporal Epidemiology, vol. 55, no. 100756, pp. 1-11. https://doi.org/10.1016/j.sste.2025.100756.1877-5845 (print)1877-5853 (online)10.1016/j.sste.2025.100756http://hdl.handle.net/2263/105003Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using environmental similarity analysis to assess transferability. Results show moderate-to-high climatic analogues in northern Kenya, eastern Uganda, and South Sudan. Between 2017 and 2020, predicted tularemia suitability increased by a median of +0.18 compared to the 2012–2015 baseline, particularly in arid and semi-arid zones. Low interannual variability suggests persistent climatic suitability. A thermal plausibility test showed strong agreement (r = 0.82) between predicted risk and the Gaussian thermal profile of Francisella tularensis. Our findings suggest that climate alone can spatially explain tularemia risk across Africa’s drylands. This method provides a transferable framework for early warning in data-poor regions and supports anticipatory surveillance in the context of climate change.en© 2025 The Author(s). This is an open access article under the CC BY-NC-ND license.Zoonotic diseaseTick-borne diseasesFix effect regressionDeer fly fever transmissionDecision makingClimate impactArticle