Poisson sampling: a sampling strategy for concurrently establishing freedom from disease and estimating population characteristics.

Abstract

Surveys of animal populations are often designed to either demonstrate freedom from disease or to estimate parameters that describe the population, such as disease prevalence, proportion of vaccinated animals, or average animal weight and value. Targeted surveillance is a sampling approach where animals are selected for testing based on the presence of characteristics that indicate a higher probability of disease. This approach can substantially reduce the sample size that is required to demonstrate freedom from disease, but inferences about other population parameters are generally not possible because the sample design often lacks the properties required for making inferences in a traditional survey sample. Determining which animals to sample can also be difficult when either more than one characteristic exists or the characteristic is a continuous attribute, such as age or weight. Poisson sampling is an unequal probability sampling design that can provide efficiencies similar to targeted surveillance while allowing inferences for other population parameters. The adaptation of Poisson sampling to animal surveys is described. A simulation study, based on sampling a flock of sheep, is used to demonstrate the reductions in sample size that are possible with Poisson sampling. The study showed that the sample size required for a flock-level sensitivity of 0.95 when using Poisson sampling was less than half that required when using simple random sampling. The performance of estimators for prevalence of scrapie and distribution of genotypes are also compared.