Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics Public Deposited

http://ir.library.oregonstate.edu/concern/articles/3r074w87h

To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The article was published by the Public Library of Science and is in the public domain. The published article can be found at:  http://www.plosone.org/.

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  • Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon Oncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.
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  • Colvin, M. E., Peterson, J. T., Kent, M. L., & Schreck, C. B. (2015). Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics. PloS ONE, 10(3), e0116605. doi:10.1371/journal.pone.0116605
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  • description.provenance : Made available in DSpace on 2015-04-30T18:40:51Z (GMT). No. of bitstreams: 3 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) ColvinMichaelFisheriesWildlifeOccupancyModelingImproved.pdf: 556405 bytes, checksum: b4253ce5b6027bd858e7e54244152de5 (MD5) ColvinMichaelFisheriesWildlifeOccupancyModelingImprovedSupportingInformationS1.PDF: 60516 bytes, checksum: 9fea3c1aa2060bdeb4389bab2c6076f4 (MD5) Previous issue date: 2015-03-04
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