Peer-reviewed veterinary case report
Use of a Markov-chain Monte Carlo model to evaluate the time value of historical testing information in animal populations.
- Journal:
- Preventive veterinary medicine
- Year:
- 2001
- Authors:
- Schlosser, W & Ebel, E
- Affiliation:
- College Station · United States
Plain-English summary
This study looks at how to better use past testing information when assessing the risk of infectious diseases in animals. Current methods often overlook the importance of both old and new data. Researchers developed a special model that takes into account the unique features of diseases and the differences among animals and herds, which helps provide a clearer understanding of the value of historical testing information. This approach can be useful in areas like trade, food safety, and regulations related to the health of pets and farm animals. Overall, the new model aims to improve how we evaluate past disease data for making informed decisions.
Abstract
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.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/11182461/