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Climate change impacts on Zika and dengue risk in four Brazilian cities: projections using a temperature-dependent basic reproduction number

For vectorborne diseases the basic reproduction number R0, a measure of a diseases epidemic potential, is highly temperature dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a temperature-dependent R0(T), derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential. We obtained historical temperature data for the 5-year period 2015-2019 and projections for 2045-2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and Sao Paulo) that represent diverse climatic regions. Our model predicts that the R0(T) for Zika peaks at 2.7 around 30?C, while for dengue it peaks at 6.8 around 31?C. We find that the epidemic potential of Zika and dengue will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual R0 range will increase from 2.1-2.5, to 2.3-2.7, for Recife we project an increase from 0.4-1.9 to 0.6-2.3, for Rio de Janeiro from 0-1.9 to 0-2.3, and for Sao Paulo from 0-0.3 to 0-0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.