Biologists have a new way to determine how common a disease is in wild animals and decide how many samples they need to study. This matters because wildlife agencies often need more money or people to collect many samples and understand how much disease has spread. Monitoring certain species is crucial to prevent diseases like COVID-19, which can jump from animals to humans, and having an efficient method helps keep track of these potential threats.
Traditionally, sample size calculations assume animals in a population get sick independently. In reality, animals often gather in family groups and share space, increasing the chance of spreading diseases. If we can randomly sample from the population, testing one deer in a family group can indicate if others are also infected since they are connected. This means we might need fewer samples to understand disease prevalence, thanks to the correlation between individuals in clusters. This insight is crucial for biologists studying diseases in wildlife populations.
Krysten Schuler, assistant research professor in the Department of Public and Ecosystem Health in the College of Veterinary Medicine, said, “The framework is so flexible, we can use it for any animals. Let’s think about birds migrating and being in huge flocks versus a moose that might be solitary and not interacting in groups. That impacts what our sample size should be.”
Schuler co-authors a study on estimating disease prevalence in wildlife populations. The method explained in the Journal of Agricultural, Biological, and Environmental Statistics requires a contagious disease, predictable wildlife clustering, and random sample collection from various clusters for optimal effectiveness. James Booth, a professor in Agriculture and Life Sciences and Computing and Information Science, is the study’s first author.
The study from Cornell University focusing on Chronic Wasting Disease (CWD) in deer, explored a method considering the animals’ tendency to cluster in family groups and CWD’s high infectiousness. However, a challenge is that field biologists may need help conducting random sampling, affecting the method’s practicality. To address this, an upcoming online app could assist biologists by considering factors like natural history, breeding, and disease spread. Once available, the app would estimate the number of individuals to sample for a realistic understanding of disease prevalence.
In conclusion, this study introduces an innovative method that considers wildlife clustering behavior, providing a potential breakthrough in estimating disease prevalence. The development of an accompanying app further promises to enhance the practical application of this method, offering valuable insights for wildlife disease monitoring and management.
Journal reference:
- Booth, J.G., Hanley, B.J., Hodel, F.H. et al. Sample Size for Estimating Disease Prevalence in Free-Ranging Wildlife Populations: A Bayesian Modeling Approach. Journal of Agricultural, Biological and Environmental Statistics. DOI: 10.1007/s13253-023-00578-7.