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Inferring seasonal infection risk at population and regional scales from serology samples
Author(s) -
Wilber Mark Q.,
Webb Colleen T.,
Cunningham Fred L.,
Pedersen Kerri,
Wan XiuFeng,
Pepin Kim M.
Publication year - 2020
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1002/ecy.2882
Subject(s) - serology , wildlife disease , population , risk of infection , sampling (signal processing) , wildlife , disease , sample (material) , geography , biology , demography , environmental health , immunology , antibody , ecology , medicine , computer science , chemistry , genetics , filter (signal processing) , chromatography , sociology , computer vision
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.