Massachusetts Project: Improving management and surveillance decisions related to white nose syndrome by accounting for imperfect detection and misclassification error

Massachusetts Project

Improving management and surveillance decisions related to white nose syndrome by accounting for imperfect detection and misclassification error

June 2021 - May 2025

Personnel

Participating Agencies

FY21 Biological Threats RFP

Management decisions for emerging infectious diseases, endangered species, and invasive species depend on the true state of the system and must be made quickly to maximize opportunities for mitigation. However, molecular detection methods, like any diagnostic test used in the detection of pathogens, may occasionally yield false negatives (i.e., when a sample appears negative but is truly positive) as well as ‘ambiguous,’ ‘uncertain,’ or ‘equivocal’ results (i.e., an inability to confidently classify a sample as negative or positive). Here, we will construct a hierarchical Bayesian model to account for imperfect pathogen detection and ambiguity in the white nose syndrome (WNS) system; our model will use surveillance data to improve forecasts of the invasion front, improve state-dependent decision making, and improve our ability to identify high priority areas for WNS surveillance. We foresee our model also improving the management and decision-making process of other pressing issues which benefit from early detection but are challenged by imperfect and ambiguous detections, including those related to invasive species, endangered species, and pathogen surveillance.

Research Publications

Research Publications	Publication Date
Grant, E. H. C., Mummah, R., Mosher, B. A., Evans, J., & G. V. DiRenzo. 2023. Inferring pathogen presence and prevalence under sample misclassification and partial observation. Methods in Ecology & Evolution. https://doi.org/10.1111/2041-210X.14102. \| Abstract	April 2023	Surveillance programs are essential for detecting emerging pathogens and often rely on molecular methods to make inference about the presence of a target disease agent. However, molecular methods rarely detect target DNA perfectly. For example, molecular pathogen detection methods can result in misclassification (i.e., false positives and false negatives) or partial detection errors (i.e., detections with ‘ambiguous’, ‘uncertain’, or ‘equivocal’ results). Typically, these partial observations are either discarded or censored from the data; this, however, disregards information that may be exploited to make inference to the true state. Here, we develop a Bayesian hierarchal framework that accommodates false negative, false positive, and uncertain detections to improve inference to the occupancy of a pathogen. We apply our modeling framework to a case study of the fungal pathogen <i>Pseudogymnoascus destructans</i> (Pd) identified in Texas bats at the invasion front of white-nose syndrome. To improve future surveillance programs, we provide guidance on sample sizes required to be 95% certain a target organism is absent from a site. We found that the presence of uncertain detections increased the variability of resulting posterior probability distributions of pathogen occurrence, and estimates of required sample size were very sensitive to prior information about occupancy and prevalence. In the Pd case study, we found that the posterior probability of occupancy was very low in 2018, but approached 1 in 2020, reflecting increasing prior probabilities of occupancy and prevalence elicited from a manager.Our modeling framework provides the user a posterior probability distribution of pathogen occurrence, which allows for subjective interpretation by the decision-maker. To help readers apply and use the methods we developed, we also provide an interactive RShiny app that generates target species occupancy estimation and sample size estimates to make these methods more accessible to the scientific community. This modeling framework and sample size guide will be useful to improving inferences from molecular surveillance data about emerging pathogens, non-native invasive species, and endangered species where misclassifications and ambiguous detections are known to occur.
DiRenzo, G. V., Hanks, E., & D. A. W. Miller. 2022. A practical guide to understanding and validating complex statistical models using simulations. Methods in Ecology & Evolution 00: 1–15. DOI: 10.1111/2041-210X.14030.	November 2022

Cooperative Fish and Wildlife Research Units Program: Massachusetts Education, Research and Technical Assistance for Managing Our Natural Resources