The goat industry in Australia: Using Bayesian network analysis to understand vulnerability to a foot and mouth disease outbreak

Manyweathers, Jennifer; Maru, Yiheyis; Hayes, Lynne; Loechel, Barton; Kruger, Heleen; Mankad, Aditi; Xie, Gang; Woodgate, Robert; Hernández‐Jover, Marta

doi:10.1016/j.prevetmed.2020.105236

Cited by 2 publications

(5 citation statements)

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“…When considering the disease hotline as part of the early response system available to livestock producers, sheep producers who were part of this study were very unlikely to use the disease hotline if they saw anything unusual in their animals, regardless of location or vulnerability level. This reported low uptake is also replicated in the study of beef producer vulnerability (22) and goat producers (29) and does suggest that adoption of this resource as a reporting and support tool might benefit from further examination of barriers to adoption and subsequent strengthening of its capability and usefulness to producers.…”

Section: Discussionmentioning

confidence: 68%

“…A cross-sectional study was designed, and a questionnaire was developed to gather quantitative data to build a vulnerabilitybased typology of the Australian sheep industry, using FMD as a model. The methodological approach used for this study was the same of that used in Manyweathers et al (22,29). All research activities were approved by the Human Research Ethics Committee at Charles Sturt University (H400201720).…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we used the most popular commercial BN software Netica (version 6.05) (34) to characterize Australian sheep producers based on their enterprises' vulnerability to an FMD outbreak. This approach has also been used by beef and goat producers (22,29).…”

Section: Bayesian Network Analysismentioning

confidence: 99%

“…The qualitative descriptors were determined based on biosecurity and EAD management literature and historical epidemiological evidence from previous FMD outbreaks (2,17,23). Based on the sensitivity analysis results, some variables in the initial model were removed from the final model due to their lack of influence (22,29).…”

Section: Bayesian Network Analysismentioning

confidence: 99%

“…This paper focuses on Australian sheep producers' beliefs and practices that may influence their likelihood of exposure and capacity to respond to an FMD outbreak. The results pertaining to the Australian beef and goat industries have been reported elsewhere (22,29).…”

Section: Introductionmentioning

confidence: 96%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 68%

Section: Methodsmentioning

confidence: 99%

Section: Bayesian Network Analysismentioning

confidence: 99%