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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, Rob; Hernandez-Jover, Marta.
Afiliação
  • Manyweathers J; Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga, NSW 2678, Australia. Electronic address: jmanyweathers@csu.edu.au.
  • Maru Y; Commonwealth Scientific and Industrial Research Organisation, Canberra, ACT 2601, Australia.
  • Hayes L; School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
  • Loechel B; Commonwealth Scientific and Industrial Research Organisation, Brisbane QLD 4001, Australia.
  • Kruger H; Australian Bureau of Agricultural and Resource Economics and Science, Canberra ACT 2601, Australia.
  • Mankad A; Commonwealth Scientific and Industrial Research Organisation, Brisbane QLD 4001, Australia.
  • Xie G; Quantitative Consulting Unit, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
  • Woodgate R; Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga, NSW 2678, Australia; School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
  • Hernandez-Jover M; Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University), Wagga Wagga, NSW 2678, Australia; School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, NSW 2678, Australia.
Prev Vet Med ; 175: 104872, 2020 Feb.
Article em En | MEDLINE | ID: mdl-31981953
ABSTRACT
Effective and adaptable biosecurity and surveillance systems are crucial for maintaining and increasing Australia's competitive advantages in international markets, and for the production of high quality, safe animal products. These systems are continuously strengthened by ongoing government and industry investment. However, a better understanding of evolving disease risks and the country's capacity to respond to these risks is needed. This study developed a vulnerability framework based on characteristics and behaviours of livestock producers that impact exposure and response capacity to an emergency animal disease (EAD) outbreak among beef producers in Australia, with a focus on foot and mouth disease (FMD). This framework articulated producer vulnerability typologies to better inform surveillance resource allocation and future research direction. A cross-sectional study of beef producers in Australia was conducted to gather information on producers' demographics, husbandry characteristics, biosecurity and animal health management practices and beliefs, including those specific to FMD risk and response capacity. A Bayesian Network (BN) model was developed from the vulnerability framework, to investigate the complex interrelationships between variables and identify producer typologies. A total of 375 usable responses were obtained from the cross-sectional study. Regarding EAD exposure, producers implemented appropriate biosecurity practices for incoming stock, such as isolation (72.0 %), inspection for disease (88.7 %) and the use of vendor declarations (78.5 %); however, other biosecurity practices were limited, such as restriction of visitor access, visitor biosecurity requirements or feral animal control. In relation to response capacity, a moderate uptake of practices was observed. Whilst daily or weekly visual inspection of animals was reported by most producers (90.1 %), physical inspection was less frequent. Most producers would call a private veterinarian in response to unusual signs of disease in their cattle; however, over 40 % of producers did not cite calling a government veterinarian as a priority action. Most producers believe an FMD outbreak would have extremely serious consequences; however, their level of concern was moderate and their confidence in identifying FMD symptoms was low. 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 model identified property size, number of cattle and exposure variables as the most influential to the overall producer vulnerability. Results from this study can inform how to best use current biosecurity and surveillance resources and identify where opportunities exist for improving Australia's preparedness for future EAD incursions.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças dos Bovinos / Conhecimentos, Atitudes e Prática em Saúde / Surtos de Doenças / Febre Aftosa / Criação de Animais Domésticos Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças dos Bovinos / Conhecimentos, Atitudes e Prática em Saúde / Surtos de Doenças / Febre Aftosa / Criação de Animais Domésticos Idioma: En Ano de publicação: 2020 Tipo de documento: Article