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Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making

The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities...

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Autores principales: Pillai, Segaran P., Fruetel, Julia A., Anderson, Kevin, Levinson, Rebecca, Hernandez, Patricia, Heimer, Brandon, Morse, Stephen A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204104/
https://www.ncbi.nlm.nih.gov/pubmed/35721853
http://dx.doi.org/10.3389/fbioe.2022.756586
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author Pillai, Segaran P.
Fruetel, Julia A.
Anderson, Kevin
Levinson, Rebecca
Hernandez, Patricia
Heimer, Brandon
Morse, Stephen A.
author_facet Pillai, Segaran P.
Fruetel, Julia A.
Anderson, Kevin
Levinson, Rebecca
Hernandez, Patricia
Heimer, Brandon
Morse, Stephen A.
author_sort Pillai, Segaran P.
collection PubMed
description The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human 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. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding “by eye” may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions.
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spelling pubmed-92041042022-06-18 Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making Pillai, Segaran P. Fruetel, Julia A. Anderson, Kevin Levinson, Rebecca Hernandez, Patricia Heimer, Brandon Morse, Stephen A. Front Bioeng Biotechnol Bioengineering and Biotechnology The Centers for Disease Control and Prevention (CDC) Select Agent Program establishes a list of biological agents and toxins that potentially threaten public health and safety, the procedures governing the possession, utilization, and transfer of those agents, and training requirements for entities working with them. Every 2 years the Program reviews the select agent list, utilizing subject matter expert (SME) assessments to rank the agents. In this study, we explore the applicability of multi-criteria decision analysis (MCDA) techniques and logic tree analysis to support the CDC Select Agent Program biennial review process, applying the approach broadly to include non-select agents to evaluate its generality. We conducted a literature search for over 70 pathogens against 15 criteria for assessing public health and bioterrorism risk and documented the findings for archiving. The most prominent data gaps were found for aerosol stability and human 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. Analysis of results obtained from a two-dimensional plot of weighted scores for difficulty of attack (i.e., exposure and production criteria) vs. consequences of an attack (i.e., consequence and mitigation criteria) provided greater fidelity for understanding agent placement compared to a 1-to-n ranking and was used to define a region in the upper right-hand quadrant for identifying pathogens for consideration as select agents. A sensitivity analysis varied the numerical weights attributed to various properties of the pathogens to identify potential quantitative (x and y) thresholds for classifying select agents. The results indicate while there is some clustering of agent scores to suggest thresholds, there are still pathogens that score close to any threshold, suggesting that thresholding “by eye” may not be sufficient. The sensitivity analysis indicates quantitative thresholds are plausible, and there is good agreement of the analytical results with select agent designations. A second analytical approach that applied the data using a logic tree format to rule out pathogens for consideration as select agents arrived at similar conclusions. Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9204104/ /pubmed/35721853 http://dx.doi.org/10.3389/fbioe.2022.756586 Text en Copyright © 2022 Pillai, Fruetel, Anderson, Levinson, Hernandez, Heimer 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.
Fruetel, Julia A.
Anderson, Kevin
Levinson, Rebecca
Hernandez, Patricia
Heimer, Brandon
Morse, Stephen A.
Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title_full Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title_fullStr Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title_full_unstemmed Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title_short Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision Making
title_sort application of multi-criteria decision analysis techniques for informing select agent designation and decision making
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204104/
https://www.ncbi.nlm.nih.gov/pubmed/35721853
http://dx.doi.org/10.3389/fbioe.2022.756586
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