Climate explains geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics, but when the underlying mechanisms are unresolved, studies can lead to seemingly context-dependent effects of climate on natural populations. Climate-sensitive vector-borne diseases such as dengue, chikungunya, and Zika are one example where climate appears to have opposing effects in different contexts. In this study, our objective was to test the extent to which a mathematical model, parameterized with climate-driven mosquito physiology measured in laboratory studies, predicts observed vector and disease dynamics in the field across ecologically and culturally distinct settings in Ecuador and Kenya. The model incorporates different rain functions and time lags. We show that the climate-driven model generates a range of disease dynamics consistent with observations of abundances and laboratory-confirmed arboviral incidence with varying levels of accuracy (28 – 85% for vector dynamics, 36 – 88% for human disease dynamics). Further, we find that the model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and a larger proportion of homes without window screens and made of cement; the model predicted human disease dynamics better in sites with lower annual rainfall and a larger proportion of homes that spray insecticide. A mechanistic model that robustly captures the influence of climate on viruses transmitted by provides critical information to help guide future intervention efforts and improve climate change predictions.