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Ecol Evol. 2019 Aug 27;9(18):10404-10414. doi: 10.1002/ece3.5558. eCollection 2019 Sep. [h=1].[/h]
Tabak MA[SUP]1[/SUP], Pedersen K[SUP]2[/SUP], Miller RS[SUP]1[/SUP].
[h=3]Author information[/h] 1 Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado. 2 Wildlife Services United States Department of Agriculture Raleigh North Carolina.
[h=3]Abstract[/h] Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America.We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection.We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence.Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters.Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
? 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
[h=4]KEYWORDS:[/h] detection probability; hierarchical Bayesian model; opportunistic sampling; pathogen; prevalence; sensitivity; specificity; wildlife
PMID: 31632645 PMCID: PMC6787870 DOI: 10.1002/ece3.5558
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Tabak MA[SUP]1[/SUP], Pedersen K[SUP]2[/SUP], Miller RS[SUP]1[/SUP].
[h=3]Author information[/h] 1 Center for Epidemiology and Animal Health United States Department of Agriculture Fort Collins Colorado. 2 Wildlife Services United States Department of Agriculture Raleigh North Carolina.
[h=3]Abstract[/h] Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America.We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection.We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence.Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters.Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
? 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
[h=4]KEYWORDS:[/h] detection probability; hierarchical Bayesian model; opportunistic sampling; pathogen; prevalence; sensitivity; specificity; wildlife
PMID: 31632645 PMCID: PMC6787870 DOI: 10.1002/ece3.5558
Free full text