Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach

Authors

  • Leah R. Johnson,

    Corresponding author
    1. Ecology and Evolution, University of Chicago, Chicago, Illinois 60637 USA
    2. Integrative Biology, University of South Florida, Tampa, Florida 33620 USA
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  • Tal Ben-Horin,

    1. Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106 USA
    2. Marine and Coastal Sciences, Rutgers University, Piscataway Township, New Jersey 08854 USA
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  • Kevin D. Lafferty,

    1. Western Ecological Research Center, U.S. Geological Survey, Sacramento, California 95819 USA
    2. Marine Science Institute, University of California, Santa Barbara, California 93106 USA
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  • Amy McNally,

    1. Geography Department, University of California, Santa Barbara, California 93106 USA
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  • Erin Mordecai,

    1. Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106 USA
    2. Biology, University of North Carolina, Chapel Hill, North Carolina 27599 USA
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  • Krijn P. Paaijmans,

    1. Barcelona Centre for International Health Research, Universitat de Barcelona, Barcelona, Spain
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  • Samraat Pawar,

    1. Ecology and Evolution, University of Chicago, Chicago, Illinois 60637 USA
    2. Department of Life Sciences, Imperial College London, Silwood Park, Ascot, Berkshire, United Kingdom
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  • Sadie J. Ryan

    1. State University of New York College of Environmental Science and Forestry (SUNY-ESF), Syracuse, New York 13210 USA
    2. State University of New York Upstate Medical University (SUNY-UMU), Syracuse, New York 13210 USA
    3. School of Life Sciences College of Agriculture, Engineering, and Science, University of KwaZulu-Natal, Durban, South Africa
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  • Corresponding Editor: K. P. Huyvaert.

Abstract

Extrinsic environmental factors influence the distribution and population dynamics of many organisms, including insects that are of concern for human health and agriculture. This is particularly true for vector-borne infectious diseases like malaria, which is a major source of morbidity and mortality in humans. Understanding the mechanistic links between environment and population processes for these diseases is key to predicting the consequences of climate change on transmission and for developing effective interventions. An important measure of the intensity of disease transmission is the reproductive number R0. However, understanding the mechanisms linking R0 and temperature, an environmental factor driving disease risk, can be challenging because the data available for parameterization are often poor. To address this, we show how a Bayesian approach can help identify critical uncertainties in components of R0 and how this uncertainty is propagated into the estimate of R0. Most notably, we find that different parameters dominate the uncertainty at different temperature regimes: bite rate from 15°C to 25°C; fecundity across all temperatures, but especially ~25–32°C; mortality from 20°C to 30°C; parasite development rate at ~15–16°C and again at ~33–35°C. Focusing empirical studies on these parameters and corresponding temperature ranges would be the most efficient way to improve estimates of R0. While we focus on malaria, our methods apply to improving process-based models more generally, including epidemiological, physiological niche, and species distribution models.

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