Climate change and the future of vector-borne disease transmission


Meeting Abstract

S3-8  Monday, Jan. 4 13:30  Climate change and the future of vector-borne disease transmission MORDECAI, E.A.*; WEIKEL, D.P.; GUDAPATI, P.; JOHNSON, L.R.; STEWART-IBARRA, A.; RYAN, S.J.; Stanford University; University of Michigan; Stanford University; University of South Florida; SUNY Upstate Medical University; University of Florida emordeca@stanford.edu http://mordecailab.com

The geographic distribution of vector-borne diseases is shaped by many ecological and evolutionary factors, including the response of vectors and pathogens to environmental drivers. Environmental temperature strongly influences vector transmission by changing rates of development, reproduction, and survival of pathogens and their vectors. Global change could thus lead to substantial changes in disease distributions. Recent work on falciparum malaria showed that the influence of temperature on transmission is nonlinear. Including these nonlinear thermal responses is critical for accurately predicting transmission in the field. Here, we show that nonlinear thermal responses are also important for dengue, chikungunya, and yellow fever transmission by Aedes aegypti and Ae. albopocitus mosquitoes. Although often considered tropical diseases, dengue, yellow fever, and chikungunya, like malaria, peak in transmission potential at intermediate temperatures of 25-29 degrees C, and decline steeply above 32-36 degrees C and below 15-17 degrees C. The model predictions match field case data much more accurately than previous models that used less realistic thermal responses. We then quantify sources of uncertainty in the model predictions and make prescriptions for future experimental work to resolve this uncertainty. Finally, we discuss implications of nonlinear thermal responses for potential shifts in transmission intensity with future climate change. In addition to predicting shifts in transmission of malaria, dengue, chikungunya, and yellow fever across temperatures, this data-driven modeling approach can inform temperature predictions for other mosquito- and fly- transmitted diseases.

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