Abstract Cholera is highly climate sensitive, however previous attempts to model its future under climate change have been limited to statistical analyses. Mechanistic models are an essential addition because they permit a deeper understanding of the complex feedback loops involved in infectious disease transmission, allowing for better modelling of potential scenarios such as interventions or changes in pathogen dynamics. We compare four mathematical models with differing assumptions of climate sensitivity and fit them to a cholera dataset from Kolkata, India using MCMC. We then use bias-corrected climate projections of temperature and rainfall from 10 independent global climate models to produce climate-based cholera projections for the period 2080–2099. Using both temperature and rainfall as inputs, the best performing model recreates seasonal patterns highly effectively. Future projections suggest an average increase in cholera cases ranging from 81% − 150% due to climate change by 2080–2099 with earlier peaks in the infection cycle likely due to heightened transmission rates earlier in the year. Sensitivity analysis reveals that uncertainties in parameters related to the contact rate and water dynamics have the greatest impact on model projections, suggesting that these factors are critical for refining future predictions. While our mechanistic model demonstrates the potential to project cholera dynamics under future climate scenarios, projections remain sensitive to key knowledge gaps including epidemiological parameters and effects of temperature on bacterial growth. Addressing these limitations through improved environmental observations and more detailed process representation will be essential for refining future climate-cholera projections and informing long-term control strategies.
Shackleton et al. (Sun,) studied this question.