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Ecol Lett. 2017 Mar;20(3):275-292. doi: 10.1111/ele.12732. Epub 2017 Jan 16.

Inferring infection hazard in wildlife populations by linking data across individual and population scales.

Author information

1
National Wildlife Research Center, United States Department of Agriculture, 4101 Laporte Ave., Fort Collins, CO, 80521, USA.
2
Department of Biology, Colorado State University, Fort Collins, CO, 80523, USA.
3
Animal and Plant Health Inspection Service, United States Department of Agriculture, Veterinary Services, 2155 Center Drive, Building B, Fort Collins, CO, 80523, USA.
4
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA.
5
MRC Centre for Outbreak Analysis and Modelling, Imperial College, London, UK.
6
U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way, Bozeman, MT, 59715, USA.
7
U. S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, 1630 Linden Drove, University of Wisconsin, Madison, WI, 53706, USA.
8
U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit; Departments of Fish, Wildlife, & Conservation Biology and Statistics, Colorado State University, 1484 Campus Delivery, Fort Collins, CO, 80523, USA.
9
Department of Statistics, Colorado State University, Fort Collins, CO, 80523, USA.
10
Department of Ecology & Evolutionary Biology, UCLA, Los Angeles, CA, 90095, USA.

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.

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
[Indexed for MEDLINE]

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