Using a Bayesian Network Predictive Model to Understand Vulnerability of Australian Sheep Producers to a Foot and Mouth Disease Outbreak

Abstract

To maintain and strengthen Australia's competitive international advantage in sheep meat and wool markets, the biosecurity systems that support these industries need to be robust and effective. These systems, strengthened by jurisdictional and livestock industry investments, can also be enhanced by a deeper understanding of individual producer risk of exposure to animal diseases and capacity to respond to these risks. This observational study developed a Vulnerability framework, built from current data from Australian sheep producers around behaviors and beliefs that may impact on their likelihood of Exposure and Response Capacity (willingness and ability to respond) to an emergency animal disease (EAD). Using foot and mouth disease (FMD) as a model, a cross-sectional survey gathered information on sheep producers' demographics, and their practices and beliefs around animal health management and biosecurity. Using the Vulnerability framework, a Bayesian Network (BN) model was developed as a first attempt to develop a decision making tool to inform risk based surveillance resource allocation. Populated by the data from 448 completed questionnaires, the BN model was analyzed to investigate relationships between variables and develop producer Vulnerability profiles. Respondents reported high levels of implementation of biosecurity practices that impact the likelihood of exposure to an EAD, such as the use of appropriate animal movement documentation (75.4%) and isolation of incoming stock (64.9%). However, adoption of other practices relating to feral animal control and biosecurity protocols for visitors were limited. Respondents reported a high uptake of Response Capacity practices, including identifying themselves as responsible for observing (94.6%), reporting unusual signs of disease in their animals (91.0%) and daily/weekly inspection of animals (90.0%). The BN analysis identified six Vulnerability typologies, with three levels of Exposure (high, moderate, low) and two levels of Response Capacity (high, low), as described by producer demographics and practices. The most influential Exposure variables on producer Vulnerability included adoption levels of visitor biosecurity and visitor access protocols. Findings from this study can guide decisions around resource allocation to improve Australia's readiness for EAD incursion and strengthen the country's biosecurity system.

Effects of wearable therapies on jump performance in sport horses

“…In consideration of the complex and interconnected nature of equine physiological and biomechanical responses, relationships between variables were explored via a Bayesian learning network (BLN). While a novel approach to this specific type of investigation in equines, Bayesian networks have been successfully applied in other specialties of animal and veterinary science, including nutrition ( 19 ), reproduction ( 20 ), lactation ( 21 ), genetics ( 22 ), and epidemiology ( 23 , 24 ). Bayesian networks possess several advantages over traditional linear models for analyzing highly variable responses that are difficult to interpret in isolation.…”

Critically appraised topic: the use of vaccination to control the spread of foot‐and‐mouth disease in Australian livestock in the event of an incursion

“…Studies undertaken relating to FMD in Australia are predominantly model simulations performed in an attempt to predict various outcomes and impacts of an outbreak. Many of these studies consider issues regarding surveillance and incursion detection, 8,9 biosecurity risks and farm vulnerability, 10 predicted transmission and spread 11 and disease control 12 . Models can be used to predict the impact and to inform policymakers regarding transboundary diseases.…”

“…issues regarding surveillance and incursion detection, 8,9 biosecurity risks and farm vulnerability, 10 predicted transmission and spread 11 and disease control. 12 Models can be used to predict the impact and to inform policymakers regarding transboundary diseases.…”