BRIDGING SCALES IN THE EVOLUTION OF INFECTIOUS DISEASE LIFE HISTORIES: APPLICATION

Authors

  • Nicole Mideo,

    1. Centre for Immunity, Infection, and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom
    2. E-mail: N.Mideo@ed.ac.uk
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  • William A. Nelson,

    1. Department of Biology, Queen’s University, Kingston, ON, K7L 3N6, Canada
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  • Sarah E. Reece,

    1. Centre for Immunity, Infection, and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom
    2. Institutes of Evolution, Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom
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  • Andrew S. Bell,

    1. Center for Infectious Disease Dynamics, Departments of Biology and Entomology, Pennsylvania State University, University Park, PA 16827
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  • Andrew F. Read,

    1. Center for Infectious Disease Dynamics, Departments of Biology and Entomology, Pennsylvania State University, University Park, PA 16827
    2. Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
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  • Troy Day

    1. Department of Biology, Queen’s University, Kingston, ON, K7L 3N6, Canada
    2. Department of Mathematics and Statistics, Queen’s University, Kingston, ON, K7L 3N6, Canada
    3. E-mail: tday@mast.queensu.ca
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Abstract

Within- and between-host disease processes occur on the same timescales, therefore changes in the within-host dynamics of parasites, resources, and immunity can interact with changes in the epidemiological dynamics to affect evolutionary outcomes. Consequently, studies of the evolution of disease life histories, that is, infection-age-specific patterns of transmission and virulence, have been constrained by the need for a mechanistic understanding of within-host disease dynamics. In a companion paper (Day et al. 2011), we develop a novel approach that quantifies the relevant within-host aspects of disease through genetic covariance functions. Here, we demonstrate how to apply this theory to data. Using two previously published datasets from rodent malaria infections, we show how to translate experimental measures into disease life-history traits, and how to quantify the covariance in these traits. Our results show how patterns of covariance can interact with epidemiological dynamics to affect evolutionary predictions for disease life history. We also find that the selective constraints on disease life-history evolution can vary qualitatively, and that “simple” virulence-transmission trade-offs that are often the subject of experimental investigation can be obscured by trade-offs within one trait alone. Finally, we highlight the type and quality of data required for future applications.

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