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Abstract Controlling persistent infectious disease in wildlife populations is an ongoing challenge for wildlife managers and conservationists worldwide, and chronic diseases in particular remain a pernicious problem.Here, we develop a dynamic pathogen transmission model capturing key features ofMycoplasma ovipneumoniaeinfection, a major cause of population declines in North American bighorn sheepOvis canadensis. We explore the effects of model assumptions and parameter values on disease dynamics, including density‐ versus frequency‐dependent transmission, the inclusion of a carrier class versus a longer infectious period, host survival rates, disease‐induced mortality and recovery rates and the epidemic growth rate. Along the way, we estimate the basic reproductive ratio,R0, forM. ovipneumoniaein bighorn sheep to fall between approximately 1.36 and 1.74.We apply the model to compare efficacies across a suite of management actions following an epidemic, including test‐and‐remove, depopulation‐and‐reintroduction, range expansion, herd augmentation and density reduction.Our results suggest that test‐and‐remove, depopulation‐and‐reintroduction and range expansion could help persistently infected bighorn sheep herds recovery following an epidemic. By contrast, augmentation could lead to worse outcomes than those expected in the absence of management. Other management actions that improve host survival or reduce disease‐induced mortality are also likely to improve population size and persistence of chronically infected herds.Synthesis and applications. Dynamic transmission models like the one employed here offer a structured, logical approach for exploring hypotheses, planning field experiments and designing adaptive management. We find that management strategies that removed infected animals or isolated them within a structured metapopulation were most successful at facilitating herd recovery from a low‐prevalence, chronic pathogen. Ideally, models like ours should operate iteratively with field experiments to triangulate on better approaches for managing wildlife diseases.
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Inferring time of infection from field data using dynamic models of antibody decay
Abstract Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody‐level data being one of the most promising sources of information. The use of antibody levels to back‐calculate infection time requires the development of a host‐pathogen system‐specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data.We present a way to model antibody dynamics in a Bayesian framework that facilitates the incorporation of all available information about potential infection times and apply the model to estimate infection times of Channel Island foxes infected withLeptospira interrogans.Using simulated data, we show that the approach works well across a broad range of parameter settings and can lead to major improvements in infection time estimates that depend on system characteristics such as antibody decay rate and variation in peak antibody levels after exposure. When applied to field data we saw reductions up to 83% in the window of possible infection times.The method substantially simplifies the challenge of modelling antibody dynamics in the absence of individuals with known infection times, opens up new opportunities in wildlife disease ecology and can even be applied to cross‐sectional data once the model is trained.
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- PAR ID:
- 10467609
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 14
- Issue:
- 10
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 2654-2667
- Size(s):
- p. 2654-2667
- Sponsoring Org:
- National Science Foundation
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