Skip to main content
U.S. flag

An official website of the United States government

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