Use of a Markov-chain Monte Carlo model to evaluate the time value of historical testing information in animal populations

Quantitative risk assessments are now required to support many regulatory decisions involving infectious diseases of animals. Current methods, however, do not consider the relative values of historical and recent data. A Markov-chain model can use specific disease characteristics to estimate the present value of disease information collected in the past. Uncertainty about the disease characteristics and variability among animals and herds can be accounted for with Monte Carlo simulation modeling. This results in a transparent method of valuing historical testing information for use in risk assessments. We constructed such a model to value historical testing information in a more-transparent and -reproducible manner. Applications for this method include trade, food safety, and domestic animal-health regulations.