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Climatic warming will likely have idiosyncratic impacts on infectious diseases, causing some to increase while others decrease or shift geographically. A mechanistic framework could better predict these different temperature-disease outcomes. However, such a framework remains challenging to develop, due to the nonlinear and (sometimes) opposing thermal responses of different host and parasite traits and due to the difficulty of validating model predictions with observations and experiments. We address these challenges in a zooplankton-fungus (Daphnia dentifera-Metschnikowia bicuspidata) system. We test the hypothesis that warmer temperatures promote disease spread and produce larger epidemics. In lakes, epidemics that start earlier and warmer in autumn grow much larger. In a mesocosm experiment, warmer temperatures produced larger epidemics. A mechanistic model parameterized with trait assays revealed that this pattern arose primarily from the temperature dependence of transmission rate (β), governed by the increasing foraging (and, hence, parasite exposure) rate of hosts (f). In the trait assays, parasite production seemed sufficiently responsive to shape epidemics as well; however, this trait proved too thermally insensitive in the mesocosm experiment and lake survey to matter much. Thus, in warmer environments, increased foraging of hosts raised transmission rate, yielding bigger epidemics through a potentially general, exposure-based mechanism for ectotherms. This mechanistic approach highlights how a trait-based framework will enhance predictive insight into responses of infectious disease to a warmer world.

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34°C with maximal transmission occurring in a range from 26-29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.