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Ecol Lett. 2017 Jan 16. doi: 10.1111/ele.12732. [Epub ahead of print]
Inferring infection hazard in wildlife populations by linking data across individual and population scales.
Pepin KM1, Kay SL1, Golas BD2, Shriner SS1, Gilbert AT1, Miller RS3, Graham AL4, Riley S5, Cross PC6, Samuel MD7, Hooten MB8, Hoeting JA9, Lloyd-Smith JO10, Webb CT2, Buhnerkempe MG10.
Author information
Abstract
Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.
? 2017 John Wiley & Sons Ltd/CNRS.
KEYWORDS:
Antibody; antibody kinetics; disease hazard; force of infection; incidence; individual-level variation; influenza; serosurveillance; transmission; within-host
PMID: 28090753 DOI: 10.1111/ele.12732
[PubMed - as supplied by publisher]
Inferring infection hazard in wildlife populations by linking data across individual and population scales.
Pepin KM1, Kay SL1, Golas BD2, Shriner SS1, Gilbert AT1, Miller RS3, Graham AL4, Riley S5, Cross PC6, Samuel MD7, Hooten MB8, Hoeting JA9, Lloyd-Smith JO10, Webb CT2, Buhnerkempe MG10.
Author information
Abstract
Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.
? 2017 John Wiley & Sons Ltd/CNRS.
KEYWORDS:
Antibody; antibody kinetics; disease hazard; force of infection; incidence; individual-level variation; influenza; serosurveillance; transmission; within-host
PMID: 28090753 DOI: 10.1111/ele.12732
[PubMed - as supplied by publisher]