Diseases of livestock in the Pacific Islands region: Setting priorities for food animal biosecurity

Brioudes, Aurélie; Warner, Jeffrey; Hedlefs, Robert; Gummow, Bruce

doi:10.1016/j.actatropica.2014.12.012

Cited by 20 publications

(14 citation statements)

References 28 publications

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“…In the Pacific Islands, leptospirosis has been identified in many animal species including rodents and livestock, 38 and is considered as one of the most important livestock diseases in the region in terms of impact on human health. 39 However, the relative importance of each animal disease in human infections is currently poorly understood. Our results provide epidemiological evidence that multiple animal species are likely to be important in leptospirosis transmission in Fiji, and that the intensity of exposure to animals as well as the relative importance of each animal species vary significantly between ethnic groups and residential settings, i.e.…”

Section: Discussionmentioning

confidence: 99%

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Lau

Mayfield

Lowry

et al. 2017

Environmental Modelling & Software

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Regression models are the standard approaches used in infectious disease epidemiology, but have limited ability to represent causality or complexity. We explore Bayesian networks (BNs) as an alternative approach for modelling infectious disease transmission, using leptospirosis as an example. Data were obtained from a leptospirosis study in Fiji in 2013. We compared the performance of naïve versus expert-structured BNs for modelling the relative importance of animal species in disease transmission in different ethnic groups and residential settings. For BNs of animal exposures at the individual/household level, R 2 for predicted versus observed infection rates were 0.59 for naïve and 0.75-0.93 for structured models of ethnic groups; and 0.54 for naïve and 0.93-1.00 for structured models of residential settings. BNs provide a promising approach for modelling infectious disease transmission under complex scenarios. The relative importance of animal species varied between subgroups, with important implications for more targeted public health control strategies.

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Section: Discussionmentioning

confidence: 99%

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Lau

Mayfield

Lowry

et al. 2017

Environmental Modelling & Software

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“…Numerous approaches have, however, demonstrated the benefits of combining endemic and exotic pathogens into a single framework. For example, a prioritization study of animal diseases within the Pacific Islands region was performed that identified both exotic and endemic disease organisms in the highest priority group of organisms (Brioudes, Warner, Hedlefs, & Gummow, ). This emphasized the need for decision‐makers to carefully balance resources towards diseases that are causing immediate losses but are likely to have less severe consequences, versus diseases that have the potential to have drastic economic and social impact but are not yet a direct threat.…”

Section: Discussionmentioning

confidence: 99%

Creating a framework for the prioritization of biosecurity risks to the New Zealand dairy industry

Muellner¹,

Hodges

Ahlstrom³

et al. 2018

Transbound Emerg Dis

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The New Zealand dairy sector relies on robust biosecurity measures to control and mitigate a wide range of threats to the industry. To optimize the prioritization of organisms and manage the risk they pose to the sector in a transparent and credible way, the Dairy Biosecurity Risk Evaluation Framework (D-BRiEF) was developed. This comprehensive framework was specifically designed for decision support, using a standardized approach to address the full spectrum of biosecurity threats to the sector, including exotic and endemic animal disease organisms, pest plants and insects. D-BRiEF is underpinned by three main processes, namely (i) hazard identification; (ii) multicriteria risk assessment; and (iii) communication for risk management. Expert knowledge and empirical data, including associated uncertainty, are harnessed in a standardized format. Results feed into a probability-impact model that was developed in close collaboration with dairy sector economists to provide overall comparative 10-year quantitative economic impact estimates for each assessed risk organism. A description of the overarching framework, which applies to diverse organism groups, is presented with detailed methodology on both endemic and exotic animal disease risk organisms. Examples of visual outputs are included, although actual ranking results are not reported due to industry confidentiality. D-BRiEF can provide a decision advantage to DairyNZ biosecurity risk managers and sector stakeholders by creating a transparent process that can be interrogated and updated at multiple levels to fully understand the layers of risk posed by different organisms.

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“…Past disease prioritization studies have been conducted for different purposes, such as communicable diseases surveillance (18,19), biosecurity (20), and resource allocation (21,22), and have covered disease in humans, livestock, or wildlife. Many studies were conducted primarily at national (8-10,19,21-36) and regional (11)(12)(13)(14)16,20,(37)(38)(39)(40)(41)(42)(43) levels (e.g., Europe and North America) but rarely at a global level (44,45). None of the disease prioritization exercises matched the aims of the R&D Blueprint, its public health focus, and its global reach; thus, WHO needed to develop its own methodology.…”

Section: World Health Organization Methodology To Prioritize Emergingmentioning

confidence: 99%

World Health Organization Methodology to Prioritize Emerging Infectious Diseases in Need of Research and Development

Mehand¹,

Millett²,

Al-Shorbaji³

et al. 2018

Emerg. Infect. Dis.

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The World Health Organization R&D Blueprint aims to accelerate the availability of medical technologies during epidemics by focusing on a list of prioritized emerging diseases for which medical countermeasures are insufficient or nonexistent. The prioritization process has 3 components: a Delphi process to narrow down a list of potential priority diseases, a multicriteria decision analysis to rank the short list of diseases, and a final Delphi round to arrive at a final list of 10 diseases. A group of international experts applied this process in January 2017, resulting in a list of 10 priority diseases. The robustness of the list was tested by performing a sensitivity analysis. The new process corrected major shortcomings in the pre–R&D Blueprint approach to disease prioritization and increased confidence in the results.

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Diseases of livestock in the Pacific Islands region: Setting priorities for food animal biosecurity

Cited by 20 publications

References 28 publications

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Creating a framework for the prioritization of biosecurity risks to the New Zealand dairy industry

World Health Organization Methodology to Prioritize Emerging Infectious Diseases in Need of Research and Development

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