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Dengue is a neglected tropical vector-borne disease that is spreading rapidly and increasing worldwide. Climate change has been considered one of the primary factors contributing to this escalation. In Brazil, a vast and heterogeneous country, each epidemic has set new records, with the disease currently spreading widely across the country. In this study, we used predictive modeling techniques, grounded on a Bayesian hierarchical framework, to forecast the spatio-temporal dynamics of dengue in Brazil under future climate scenarios. We used monthly historical data on dengue incidence from 2000 to 2021, collected over 557 Brazilian microregions, to learn about its association with population, environmental, climatic, and socioeconomic conditions. By integrating projections of precipitation, minimum temperature and population based on different Shared Socioeconomic Pathway scenarios, we obtained spatially resolved projections of dengue incidence for 2021-2040 and 2041-2060. The model's predictive performance was ensured via robust cross-validation. We observed an increase in dengue incidence with rising precipitation, urban infrastructure, and Normalized Difference Water Index, and a decrease with increasing elevation and deprivation index. Minimum temperature exhibited a nonlinear and positive association behavior. Our forecasts show great regional variability. In the south region, some traditionally non-endemic microregions are expected to see a clear increase in dengue incidence rate. Conversely, other regions show geographic variability of patterns, suggesting that in some cases elevated temperatures may exceed the viable threshold for dengue transmission. Investing in reducing greenhouse gas emissions and implementing measures to address the rising incidence of dengue due to climate change is crucial.
Bermudi et al. (Mon,) studied this question.