title: Crossing the scale from within-host infection dynamics to between-host transmission fitness: a discussion of current assumptions and knowledge.
abstract: The progression of an infection within a host determines the ability of a pathogen to transmit to new hosts and to maintain itself in the population. While the general connection between the infection dynamics within a host and the population-level transmission dynamics of pathogens is widely acknowledged, a comprehensive and quantitative understanding that would allow full integration of the two scales is still lacking. Here, we provide a brief discussion of both models and data that have attempted to provide quantitative mappings from within-host infection dynamics to transmission fitness. We present a conceptual framework and provide examples of studies that have taken first steps towards development of a quantitative framework that scales from within-host infections to population-level fitness of different pathogens. We hope to illustrate some general themes, summarize some of the recent advances and-maybe most importantly-discuss gaps in our ability to bridge these scales, and to stimulate future research on this important topic.
authors.affiliation: Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA 30602, USA ahandel@uga.edu.
authors.forename: Andreas
authors.initials: A
authors.lastname: Handel
authors.affiliation: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Pejman
authors.initials: P
authors.lastname: Rohani
mesh_terms: D000818:Animals; D015658:HIV Infections / Q000635:transmission; D054884:Host-Pathogen Interactions; D006801:Humans; D007239:Infections / Q000635:transmission; D007251:Influenza, Human / Q000635:transmission; D008954:Models, Biological
keywords: computational models; infectious disease; multi-scale