Estimating Landscape Spread of a Low-prevalence Disease using Multiple Surveillance Methods

Authors

Abigail Feuka, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow
Aakash Pandey, Michigan State University
Melinda Cosgrove, Michigan Department of Natural Resources
Megan Moriarty, Michigan Department of Natural Resources
Anthony Duffiney, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Okemos, Michigan
Henry Campa III, Michigan State University
Kurt C. VerCuteren, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow
Kimberly M. Pepin, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, ColoradoFollow

ORCID IDs

Feuka https://orcid.org/0000-0002-6201-4892

Pepin https://orcid.org/0000-0002-9931-8312

Document Type

Article

Date of this Version

2024

Citation

Journal of Applied Ecology (2024): 14574

doi: 10.1111/1365-2664.14574

Handling editor: Silke Bauer

Comments

United States government work

Abstract

1. Diseases occurring at low prevalence in free-ranging wildlife can be difficult to study and predict at a landscape scale. This problem is compounded by imperfect pathogen detection.

2. We used data from multiple surveillance methods to determine factors influencing spread of a low-prevalence disease, bovine tuberculosis (bTB), among white-tailed deer (Odocoileus virginianus) in a bTB endemic area over a 10-year period. We fit dynamic occupancy models to predict spatial spread of bTB through a county in Michigan, USA, within the bTB endemic area, while accounting for imperfect detection and a suite of ecological processes potentially driving bTB spread.

3. Detection by all surveillance methods was low overall (posterior mean detection probabilities 0.01–0.03). Probability of bTB occurrence was 1.46 times as likely in the historical centre of the outbreak compared to outside. Deer density had a weakly positive effect on bTB spread.

4. The probability of a site being newly infected with bTB annually was low (posterior mean colonization probabilities 0.08–0.27), while the probability of persistence after initial infection was high (posterior mean persistence probability 0.67).

5. Synthesis and applications: We used three surveillance methods to estimate probability of presence and spread of a low-prevalence pathogen at a landscape scale under incomplete sampling coverage. Our methods are relevant for determining spatial risk of any wildlife disease that is monitored using multiple methods of surveillance, which is important for prioritization of management actions.