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Occupancy modeling for improved accuracy and understanding of pathogen prevalence and dynamics
Michael E Colvin1, James T Peterson2, Michael L Kent3
1Oregon Cooperative Fish and Wildlife Research Unit, Department of Fisheries and Wildlife, Oregon State University, 104 Nash Hall, Corvallis, Oregon, 97331, United States of America.
Plos One
|March 5, 2015
Summary
This study developed a hierarchical occupancy model to accurately estimate pathogen prevalence in Chinook salmon by accounting for imperfect test sensitivity. The model improves accuracy and reduces sample sizes needed for pathogen detection in fish populations.
Area of Science:
- Aquatic animal health
- Ecology and evolution
- Pathogen detection and diagnostics
Background:
- Pathogen detection tests often have <100% sensitivity, leading to false negatives and biased prevalence estimates.
- Histological examination is valuable for detecting multiple pathogens and host pathology but is typically less sensitive than molecular methods.
- Accurate pathogen prevalence estimation is crucial for understanding disease dynamics and managing aquatic populations.
Purpose of the Study:
- To develop a hierarchical occupancy model for estimating pathogen prevalence and tissue distribution in spring Chinook salmon.
- To estimate pathogen-specific test sensitivities and infection rates using histological data.
- To assess the impact of within-host replicate sampling on the sample sizes required for pathogen detection.
Main Methods:
- Developed a hierarchical occupancy model to analyze pathogen detection data from histological examination of spring Chinook salmon tissues.
- Examined replicate tissue samples for common pathogens: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/ metacercariae, and Renibacterium salmoninarum.
- Estimated pathogen- and tissue-specific test sensitivities and host- and organ-level infection rates.
Main Results:
- The hierarchical model provided unbiased estimates of pathogen prevalence and infection rates, which were higher than unadjusted prevalence estimates.
- Pathogen and tissue-specific test sensitivities and infection rates varied significantly among the studied pathogens.
- Accounting for test sensitivity using within-host replicate samples reduced the number of individual fish needed for reliable pathogen detection.
Conclusions:
- Hierarchical occupancy modeling offers an analytical approach to obtain unbiased pathogen prevalence estimates from low-sensitivity diagnostic tests like histology.
- This method is effective for evaluating pathogen dynamics when test sensitivity is less than 100%.
- Incorporating within-host sampling strategies can optimize resource allocation in pathogen surveillance studies.
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