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How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep

The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen...

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Detalles Bibliográficos
Autores principales: Paterson, J. Terrill, Butler, Carson, Garrott, Robert, Proffitt, Kelly
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7478830/
https://www.ncbi.nlm.nih.gov/pubmed/32898140
http://dx.doi.org/10.1371/journal.pone.0237309
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author Paterson, J. Terrill
Butler, Carson
Garrott, Robert
Proffitt, Kelly
author_facet Paterson, J. Terrill
Butler, Carson
Garrott, Robert
Proffitt, Kelly
author_sort Paterson, J. Terrill
collection PubMed
description The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring.
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spelling pubmed-74788302020-09-18 How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep Paterson, J. Terrill Butler, Carson Garrott, Robert Proffitt, Kelly PLoS One Research Article The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring. Public Library of Science 2020-09-08 /pmc/articles/PMC7478830/ /pubmed/32898140 http://dx.doi.org/10.1371/journal.pone.0237309 Text en © 2020 Paterson et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Paterson, J. Terrill
Butler, Carson
Garrott, Robert
Proffitt, Kelly
How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title_full How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title_fullStr How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title_full_unstemmed How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title_short How sure are you? A web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
title_sort how sure are you? a web-based application to confront imperfect detection of respiratory pathogens in bighorn sheep
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7478830/
https://www.ncbi.nlm.nih.gov/pubmed/32898140
http://dx.doi.org/10.1371/journal.pone.0237309
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