Weighted Surveillance

This application provides an easy-to-use interface for conducting weighted surveillance for chronic wasting disease (CWD) in white-tailed deer ( Odocoileus virginianus ) populations. Weighted surveillance is based on the simple principle that within a population there exists heterogeneity among individuals with regard to disease risk. To maximize efficiency and potentially increase the likelihood of detecting new disease foci, weighted surveillance programs exploit this heterogeneity by focusing disease detection efforts in groups most at risk. The underlying methods were orginially developed by Walsh et al. (2010) using a Frequentist statistical approach. These results were then refined and recast into a Bayesian statistical framework by Heisey et al. (2014). It is imporant to note that the use of weighted surveillance techniques requires that prior information is available to estimate heterogeneity in individual risk. A general discussion of CWD surveillance is given in Walsh et al. (2012).

This application provides two tools for use. They can be accessed by clicking on the tabs above. The first tool called 'Design' is used for planning weighted surveillance activities. The user specifies how much confidence they need, and what minimum prevalence they would like to detect in yearling males (e.g., I would like a 95% confidence of detecting at least one case if the prevalence is at least 1%). This information then provides the total number of points required to meet the specified confidence for the chosen minimum prevalence. The user can then select from the potential sources of surveillance samples, and vary the number of samples arising from each source. This provides a means of setting sampling objectives for each source to ensure the requiste number of points is reached. This tool can also be used to evaluate in real-time how close a program is to achieving its goal given the number of samples collected from each source to date.

The second tool called 'Estimation' is for use after sampling for CWD detection has occurred and no positive cases were found. It provides the means to estimate the potential underlying prevalence rate of CWD given the amount of samples collected during surveillance. Of particular interest is the upper bound of the credible interval. If the disease is present in the system, it provides the prevalence rate for which there is, for example, a 95% probability the true prevalence is below. The techniques and theory underpinning them used in this application are described in Heisey et al. (2014). This application takes advantage of the R statistical software for estimation. Heterogeneity of risk classes are based on the chronic wasting disease information collected by the Wisconsin Department of Natural Resources. Jennelle et al. 2018 provide a description of the Wisconsin estimates and provide a case study of the application of weighted surveillance to CWD detection.

Weighted Surveillance - Design

White-tailed Deer ( Odocoileus virginianus )

Specify the diagnostic test sensitivity-typically assumed 1 unless live-testing.

The target number of points:

Points you have collected with your sampling program:

Points you still need to collect with your sampling program:

Use the tabs below to input the number of samples from each of the potential sources entering the surveillance stream to determine how many points you have collected towards achieving your target.

Note: use keyboard arrow keys for fine scale adjustment
Click 'Start Over' button to clear all values.

Weighted Surveillance - Estimation

White-tailed Deer ( Odocoileus virginianus )

Model inputs:
Click button below to calculate estimates
Model outputs:
Export results
Click 'Start Over' button to clear all data values

Use the tabs below to input the number of samples from each of the potential sources entering the surveillance stream.