Model-based surveillance system design under practical constraints with application to white-nose syndrome
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Abstract
Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful prevention and control of pathogens requires knowledge of the current scope and severity of disease, as well as the ability to forecast future disease dynamics. Assessment of the current situation as well as prediction of the future conditions, rely on spatially referenced information regarding the presence or absence of a pathogen, and the prevalence of the pathogen in the population. In particular, knowledge about the location of the disease front is foundational for deploying disease countermeasures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future. We also devise an optimal surveillance system design at the disease front that takes into account the practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for the pathogen that causes white-nose syndrome.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Model-based surveillance system design under practical constraints with application to white-nose syndrome |
| Series title | Environmental and Ecological Statistics |
| DOI | 10.1007/s10651-023-00578-3 |
| Volume | 30 |
| Year Published | 2023 |
| Language | English |
| Publisher | Springer |
| Contributing office(s) | Coop Res Unit Seattle, National Wildlife Health Center |
| Description | 19 p. |
| First page | 649 |
| Last page | 667 |
| Google Analytic Metrics | Metrics page |