2020 ESA Annual Meeting (August 3 - 6)

SYMP 9 Abstract - Impacts of climate on vector-borne disease transmission

Tuesday, August 4, 2020: 3:00 PM
Erin Mordecai1, Jamie M. Caldwell2, Marissa Grossman3, Catherine Lippi4, Leah R. Johnson5, Marco Neira6, Jason R. Rohr7, Sadie J. Ryan8, Van M. Savage9, Marta Shocket10, Rachel Sippy4, Anna Stewart-Ibarra11, Matthew B. Thomas12, Oswaldo Villena13, A. Desiree LaBeaud14, Eric F. Lambin15, Melisa Shah14 and Lisa I. Couper16, (1)Department of Biology, Stanford University, Stanford, CA, (2)Hawai‘i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai‘i, Kane‘ohe, HI, (3)Perspecta, (4)University of Florida, (5)Statistics, Virginia Tech, Blacksburg, VA, (6)Pontificia Universidad Catolica de Ecuador, Ecuador, (7)Biological Sciences, University of Notre Dame, Notre Dame, IN, (8)Emerging Pathogens Institute, University of Florida, (9)Department of Biomathematics, UCLA, Los Angeles, CA, (10)Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, (11)Inter-American Institute for Global Change Research, (12)Entomology, Penn State University, University Park, PA, (13)Virginia Polytechnic Institute and State University, (14)Stanford University, (15)Woods Institute for the Environment, Stanford University, Stanford, CA, (16)Biology, Stanford University, Palo Alto, CA
Background/Question/Methods

Climate affects the distribution, organismal performance, population growth, and interactions of almost all species, including mosquitoes and ticks (vectors) that transmit pathogens. Temperature nonlinearly affects growth, life history, and interaction traits, but these multivariate effects are often overlooked in climate-driven models of vector-borne disease transmission. As a result, many recent estimates of the impact of climate change on vector-borne disease transmission may be inaccurate in magnitude and direction, and may predominantly predict widespread expansions rather than geographic and seasonal shifts in disease transmission. Here, we use a mechanistic, trait-based framework to estimate the impact of temperature on transmission (R0) for 11 different vector-borne diseases, test the model predictions against observed field data, and use the validated models to predict effects of climate change on vector-borne disease transmission.

Results/Conclusions

Across pathogens including dengue, Zika, West Nile, eastern equine encephalitis (EEE), and Ross River viruses and malaria, transmission responded strongly and unimodally to temperature, with lower thermal limits of 9-23ºC, upper thermal limits of 32-38°C, and optima of 23-29°C. Temperate pathogens generally had cooler lower thermal limits and broader thermal ranges than tropical pathogens. These mechanistic models accurately predicted entomological risk and human incidence of malaria in Africa, timing and geography of the transmission season for Ross River fever in Australia, average annual incidence of West Nile fever in the United States, probability and magnitude of outbreaks for dengue, chikungunya, and Zika in the Americas, and dynamics of Aedes aegypti mosquitoes and dengue infection through time in Kenya and Ecuador. Together, these models suggest that transmission risk will generally shift geographically and seasonally, depending on the pathogen, vector, current geographical distribution, and amount of temperature change. For example, the hotspot of malaria risk is expected to shift from coastal West and Central Africa to higher-elevation East Africa, while the hotspot for dengue and chikungunya risk is expected to broadly expand across sub-Saharan Africa. Ross River virus transmission risk is expected to expand in the temperate and more densely-populated regions of Australia. West Nile is expected to expand its transmission season and geographic range in North America. Future Lyme disease transmission is highly uncertain but expected to increase in the Northeast and Midwest U.S. More generally, impacts of climate change on vector-borne disease are nonlinear and nuanced, but predictable using mechanistic models driven by experimental and observational data.