Innovation of cost effective methods for eliminating the PRRSv from individual herds has stimulated hope that the industry may someday eliminate the virus from the U.S. However, exploiting this innovation has been hampered by the industries lack of progress on preventing the frequent transmission of the virus from one herd to another. Recently, several regional elimination projects have been initiated by producers and veterinarians to overcome this hurdle. These projects are efforts to reduce the frequency of transmission from one herd to another by better understanding what the PRRS virus is doing in the region, improving biosecurity and reducing the prevalence of the virus in the region. Surveillance is a critical element of all of these projects for better understanding what the virus is doing in the region and for measuring progress.
Although sequential testing is widely needed and used in practice for PRRSv surveillance, there is little guidance on how frequently to sample. This study provides cost effective methods for PRRSv surveillance including guidance on the frequency of sampling with a firm theoretical basis. The methodologies we develop can be expected to provide a standard framework for design and analysis of PRRSv surveillance studies. We study the disease progression and transmission of PRRS through a proposed statistical model and provide rigorous, detailed, data-based statistical framework for design and analysis of PRRS surveillance. The proposed adaptive design will help detect PRRS outbreak earlier. The methodologies developed are essential for effective control and elimination of PRRS virus on individual farms and for regional elimination projects.
We develop a web-based interface called SSF (Sample Size and Frequency), built upon the Shiny web-application framework. SSF provides easy-to-use and instantly displayed calculation of sample size and frequency based on a custom-defined scheme of their own choosing.