Understanding the vulnerability of beef producers in Australia to an FMD outbreak using a Bayesian Network predictive model

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

doi:10.1016/j.prevetmed.2019.104872

Cited by 5 publications

(12 citation statements)

References 27 publications

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“…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%

“…The questionnaire was developed with reference to existing epidemiological, behavioral, and social science research and aimed at the examination and characterization of sheep producers' vulnerability to an FMD outbreak. Further details of the design and development of the questionnaire are provided by Manyweathers et al (22). In brief, a vulnerability matrix (Figure 1) guided questionnaire development, with questions examining producers' likelihood of exposure to FMD and their response capacity (willingness and ability to inspect animals, detect and report disease) to an FMD outbreak.…”

Section: Questionnaire Designmentioning

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: 98%

“…To reduce the time taken between infection and diagnosis, understanding biosecurity beliefs and practices of producers and those regularly in contact with livestock is paramount (12,21). This includes the ability to recognize unusual signs of disease in livestock and to take appropriate steps to get a diagnosis (22). When modeling the spread of FMD from a single index farm, one of the most influential factors was found to be the ability of the producer to detect unusual signs leading to identification of FMD (23).…”

Section: Introductionmentioning

confidence: 99%

“…When modeling the spread of FMD from a single index farm, one of the most influential factors was found to be the ability of the producer to detect unusual signs leading to identification of FMD (23). Therefore, any risk-based approach to surveillance needs to be informed by stakeholder engagement, including producers' beliefs and practices around their role in general surveillance, defined in this study as monitoring for, recognizing, and reporting unusual signs of disease in their animals (22,(24)(25)(26)(27).…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Questionnaire Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%