Improving management and surveillance decisions related to white nose syndrome by accounting for imperfect detection and misclassification error
June 2021 - October 2023
- 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||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|