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Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and tr...

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Autores principales: Pillai, Segaran P., West, Todd, Anderson, Kevin, Fruetel, Julia A., McNeil, Carrie, Hernandez, Patricia, Ball, Cameron, Beck, Nataly, Morse, Stephen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10278572/
https://www.ncbi.nlm.nih.gov/pubmed/37342506
http://dx.doi.org/10.3389/fbioe.2023.1185743
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author Pillai, Segaran P.
West, Todd
Anderson, Kevin
Fruetel, Julia A.
McNeil, Carrie
Hernandez, Patricia
Ball, Cameron
Beck, Nataly
Morse, Stephen A.
author_facet Pillai, Segaran P.
West, Todd
Anderson, Kevin
Fruetel, Julia A.
McNeil, Carrie
Hernandez, Patricia
Ball, Cameron
Beck, Nataly
Morse, Stephen A.
author_sort Pillai, Segaran P.
collection PubMed
description The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.
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spelling pubmed-102785722023-06-20 Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens Pillai, Segaran P. West, Todd Anderson, Kevin Fruetel, Julia A. McNeil, Carrie Hernandez, Patricia Ball, Cameron Beck, Nataly Morse, Stephen A. Front Bioeng Biotechnol Bioengineering and Biotechnology The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making. Frontiers Media S.A. 2023-06-05 /pmc/articles/PMC10278572/ /pubmed/37342506 http://dx.doi.org/10.3389/fbioe.2023.1185743 Text en Copyright © 2023 Pillai, West, Anderson, Fruetel, McNeil, Hernandez, Ball, Beck and Morse. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Bioengineering and Biotechnology
Pillai, Segaran P.
West, Todd
Anderson, Kevin
Fruetel, Julia A.
McNeil, Carrie
Hernandez, Patricia
Ball, Cameron
Beck, Nataly
Morse, Stephen A.
Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title_full Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title_fullStr Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title_full_unstemmed Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title_short Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
title_sort application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10278572/
https://www.ncbi.nlm.nih.gov/pubmed/37342506
http://dx.doi.org/10.3389/fbioe.2023.1185743
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