TY - JOUR
T1 - On-farm evaluation of a predictive model for Australian beef and sheep producers’ vulnerability to an outbreak of foot and mouth disease
AU - Manyweathers, Jennifer
AU - Hayes, Lynne
AU - Xie, Gang
AU - Gardner, Hannah
AU - Maru, Yiheyis
AU - Woodgate, Rob
AU - Hernandez-Jover, Marta
N1 - Funding Information:
This project is supported by Meat and Livestock Australia , through funding from the Australian Government Department of Agriculture, Water and the Environment as part of its Rural R&D for Profit programme and by producer levies from Australian FMD-susceptible livestock (cattle, sheep, goats and pigs) industries and Charles Sturt University ( CSU ), leveraging significant in-kind support from the research partners. The research partners for this project are the Commonwealth Science and Industrial Research Organisation ( CSIRO ), CSU through the Graham Centre for Agricultural Innovation, the Bureau of Meteorology ( BOM ) and the Australian Department of Agriculture, Water and the Environment, supported by Animal Health Australia (AHA).
Publisher Copyright:
© 2022
PY - 2022/7
Y1 - 2022/7
N2 - To explore Australian sheep and beef producer vulnerability to an emergency animal disease outbreak, Bayesian Network models have been developed, with the ultimate goal of creating risk management tool for outbreak preparedness. These models were developed using multiple stakeholder elicitation including modelling experts, epidemiologists and on-farm stakeholders, including on-farm/survey data. An evaluation of the model's predictive capacity was conducted, using independent, blinded on-farm vulnerability assessments. Nine properties were visited, four each with sheep and beef enterprises, and one mixed enterprise. There were some discrepancies between the model predictions and on-farm assessment in the beef enterprises, with greater disparity with the sheep properties. Discrepancies between the model predictions and on-farm assessments have created opportunities for examination of the data collection process for the model development, the model itself and the on-farm assessment process. Bayesian Network approaches that allow for the inclusion of both continuous and discrete variables may improve the usefulness of these models, avoiding the loss of nuanced data by the need for discretisation of continuous variables, as will the inclusion of input from on-farm stakeholders in model development. Future work includes more data collection to improve the sensitivity of the model predictions, and a deeper, systemic exploration of the factors that may impact Australian producers’ vulnerability to an emergency animal disease outbreak.
AB - To explore Australian sheep and beef producer vulnerability to an emergency animal disease outbreak, Bayesian Network models have been developed, with the ultimate goal of creating risk management tool for outbreak preparedness. These models were developed using multiple stakeholder elicitation including modelling experts, epidemiologists and on-farm stakeholders, including on-farm/survey data. An evaluation of the model's predictive capacity was conducted, using independent, blinded on-farm vulnerability assessments. Nine properties were visited, four each with sheep and beef enterprises, and one mixed enterprise. There were some discrepancies between the model predictions and on-farm assessment in the beef enterprises, with greater disparity with the sheep properties. Discrepancies between the model predictions and on-farm assessments have created opportunities for examination of the data collection process for the model development, the model itself and the on-farm assessment process. Bayesian Network approaches that allow for the inclusion of both continuous and discrete variables may improve the usefulness of these models, avoiding the loss of nuanced data by the need for discretisation of continuous variables, as will the inclusion of input from on-farm stakeholders in model development. Future work includes more data collection to improve the sensitivity of the model predictions, and a deeper, systemic exploration of the factors that may impact Australian producers’ vulnerability to an emergency animal disease outbreak.
KW - Australian sheep and beef producers
KW - Bayesian Network model
KW - evaluation
KW - Foot and mouth disease
KW - validation
UR - http://www.scopus.com/inward/record.url?scp=85129485335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129485335&partnerID=8YFLogxK
U2 - 10.1016/j.prevetmed.2022.105656
DO - 10.1016/j.prevetmed.2022.105656
M3 - Article
C2 - 35525067
AN - SCOPUS:85129485335
VL - 204
JO - Preventive Veterinary Medicine
JF - Preventive Veterinary Medicine
SN - 0167-5877
M1 - 105656
ER -