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

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          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.

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          Most cited references45

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          The vulnerability of Australian rural communities to climate variability and change: Part II—Integrating impacts with adaptive capacity

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            Concepts for risk-based surveillance in the field of veterinary medicine and veterinary public health: Review of current approaches

            Background Emerging animal and zoonotic diseases and increasing international trade have resulted in an increased demand for veterinary surveillance systems. However, human and financial resources available to support government veterinary services are becoming more and more limited in many countries world-wide. Intuitively, issues that present higher risks merit higher priority for surveillance resources as investments will yield higher benefit-cost ratios. The rapid rate of acceptance of this core concept of risk-based surveillance has outpaced the development of its theoretical and practical bases. Discussion The principal objectives of risk-based veterinary surveillance are to identify surveillance needs to protect the health of livestock and consumers, to set priorities, and to allocate resources effectively and efficiently. An important goal is to achieve a higher benefit-cost ratio with existing or reduced resources. We propose to define risk-based surveillance systems as those that apply risk assessment methods in different steps of traditional surveillance design for early detection and management of diseases or hazards. In risk-based designs, public health, economic and trade consequences of diseases play an important role in selection of diseases or hazards. Furthermore, certain strata of the population of interest have a higher probability to be sampled for detection of diseases or hazards. Evaluation of risk-based surveillance systems shall prove that the efficacy of risk-based systems is equal or higher than traditional systems; however, the efficiency (benefit-cost ratio) shall be higher in risk-based surveillance systems. Summary Risk-based surveillance considerations are useful to support both strategic and operational decision making. This article highlights applications of risk-based surveillance systems in the veterinary field including food safety. Examples are provided for risk-based hazard selection, risk-based selection of sampling strata as well as sample size calculation based on risk considerations.
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              Internet, Phone, Mail, and Mixed‐Mode Surveys: the Tailored Design Method

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                Author and article information

                Contributors
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                11 June 2021
                2021
                : 8
                : 668679
                Affiliations
                [1] 1Graham Centre for Agricultural Innovation, Charles Sturt University , Wagga Wagga, NSW, Australia
                [2] 2School of Animal and Veterinary Sciences, Charles Sturt University , Wagga Wagga, NSW, Australia
                [3] 3Commonwealth Scientific and Industrial Research Organisation Land and Water , Canberra, ACT, Australia
                [4] 4Commonwealth Scientific and Industrial Research Organisation , Brisbane, QLD, Australia
                [5] 5Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) , Canberra, ACT, Australia
                [6] 6Quantitative Consulting Unit, Charles Sturt University , Wagga Wagga, NSW, Australia
                Author notes

                Edited by: Alejandra Victoria Capozzo, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina

                Reviewed by: Georgina Limon, Pirbright Institute, United Kingdom; Pedro Larrañaga, Polytechnic University of Madrid, Spain

                *Correspondence: Jennifer Manyweathers jmanyweathers@ 123456csu.edu.au

                This article was submitted to Veterinary Epidemiology and Economics, a section of the journal Frontiers in Veterinary Science

                Article
                10.3389/fvets.2021.668679
                8226010
                34179162
                f72b67e2-0a82-4113-9fab-09f149620f07
                Copyright © 2021 Manyweathers, Maru, Hayes, Loechel, Kruger, Mankad, Xie, Woodgate and Hernandez-Jover.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 16 February 2021
                : 22 April 2021
                Page count
                Figures: 3, Tables: 4, Equations: 0, References: 47, Pages: 12, Words: 8697
                Categories
                Veterinary Science
                Original Research

                bayesian network model,foot and mouth disease,biosecurity,vulnerability,australian sheep producers,surveillance,partnership

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