Inferring pathogen presence when sample misclassification and partial observation occur
January 23, 2023
This software contains four separate R scripts and one Matlab script that comprise an analysis to estimate the posterior probability of pathogen presence when sample misclassification and partial observations occur. We develop a Bayesian hierarchal framework that accommodates false negative, false positive, and uncertain detections and apply this framework to a case study of the fungal pathogen Pseudogymnoascus destructans (Pd) identified in Texas bats at the invasion front of white-nose syndrome. The software supports a research article submitted to Methods in Ecology and Evolution.
Citation Information
| Publication Year | 2023 |
|---|---|
| Title | Inferring pathogen presence when sample misclassification and partial observation occur |
| DOI | 10.5066/P9PDV4LV |
| Authors | Graziella V Direnzo, Evan H Grant, Riley O. Mummah, Brittany A. Mosher |
| Product Type | Software Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Cooperative Research Units Program |
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Inferring pathogen presence when sample misclassification and partial observation occur
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Inferring pathogen presence when sample misclassification and partial observation occur
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