Inferring seasonal infection risk at population and regional scales from serology samples.

Autor: Wilber MQ; Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA.; Wildlife Services, National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Fort Collins, Colorado, 80521, USA., Webb CT; Department of Biology, Colorado State University, Fort Collins, Colorado, 80523, USA., Cunningham FL; Mississippi Field Station, Wildlife Services, National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, P.O. Box 6099, Starkville, Mississippi, 39762, USA., Pedersen K; Wildlife Services, United States Department of Agriculture, Animal and Plant Health Inspection Service, 920 Main Campus Drive, Suite 200, Raleigh, North Carolina, 27606, USA., Wan XF; Missouri University Center for Research on Influenza Systems Biology, University of Missouri, Columbia, Missouri, 65211, USA.; Department of Molecular Microbiology and Immunology, School of Medicine, University of Missouri, Columbia, Missouri, 65212, USA.; Department of Electrical Engineering & Computer Science, College of Engineering, University of Missouri, Columbia, Missouri, 65211, USA.; Bond Life Sciences Center, University of Missouri, Columbia, Missouri, 65211, USA.; MU Informatics Institute, University of Missouri, Columbia, Missouri, 65211, USA.; Department of Pathobiology, College of Veterinary Medicine, University of Missouri, Columbia, Missouri, 65212, USA., Pepin KM; Wildlife Services, National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Fort Collins, Colorado, 80521, USA.
Jazyk: angličtina
Zdroj: Ecology [Ecology] 2020 Jan; Vol. 101 (1), pp. e02882. Date of Electronic Publication: 2019 Nov 19.
DOI: 10.1002/ecy.2882
Abstrakt: Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and improve understanding of the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies (serosurveillance), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock.
(© 2019 by the Ecological Society of America.)
Databáze: MEDLINE
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