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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1185743/full |
| _version_ | 1797810790839877632 |
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| author | Segaran P. Pillai Todd West Kevin Anderson Julia A. Fruetel Carrie McNeil Patricia Hernandez Cameron Ball Nataly Beck Stephen A. Morse |
| author_facet | Segaran P. Pillai Todd West Kevin Anderson Julia A. Fruetel Carrie McNeil Patricia Hernandez Cameron Ball Nataly Beck Stephen A. Morse |
| author_sort | Segaran P. Pillai |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T07:14:06Z |
| format | Article |
| id | doaj.art-9c352ccd05f34d1889f248177f69faae |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-03-13T07:14:06Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-9c352ccd05f34d1889f248177f69faae2023-06-05T13:41:28ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852023-06-011110.3389/fbioe.2023.11857431185743Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogensSegaran P. Pillai0Todd West1Kevin Anderson2Julia A. Fruetel3Carrie McNeil4Patricia Hernandez5Cameron Ball6Nataly Beck7Stephen A. Morse8Office of the Commissioner, Food and Drug Administration, Silver Spring, MD, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesScience and Technology Directorate, U.S. Department of Homeland Security, Washington, DC, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesSandia National Laboratories, U.S. Department of Energy, Livermore, CA, United StatesCenters for Disease Control and Prevention, Atlanta, GA, United StatesThe 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.https://www.frontiersin.org/articles/10.3389/fbioe.2023.1185743/fullmulti-criteria decision analysisdecision support frameworkselect agent designationagriculture animal pathogenrisk assessment tool |
| spellingShingle | Segaran P. Pillai Todd West Kevin Anderson Julia A. Fruetel Carrie McNeil Patricia Hernandez Cameron Ball Nataly Beck Stephen A. Morse Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens Frontiers in Bioengineering and Biotechnology multi-criteria decision analysis decision support framework select agent designation agriculture animal pathogen risk assessment tool |
| 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 | multi-criteria decision analysis decision support framework select agent designation agriculture animal pathogen risk assessment tool |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2023.1185743/full |
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