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.

doi:10.3389/fbioe.2022.756586

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

Journal Article · Fri Jun 03 00:00:00 EDT 2022 · Frontiers in Bioengineering and Biotechnology

DOI:https://doi.org/10.3389/fbioe.2022.756586· OSTI ID:2471769

Pillai, Segaran P. ^[1]; Fruetel, Julia A. ^[2]; Anderson, Kevin ^[3]; Levinson, Rebecca ^[2]; Hernandez, Patricia ^[2]; Heimer, Brandon ^[2]; Morse, Stephen A. ^[4]

US Food and Drug Administration (USFDA), Silver Spring, MD (United States)
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
US Dept. of Homeland Security (DHS), Washington, DC (United States)
IHRC, Inc., Atlanta, GA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: US Department of Homeland Security (DHS); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2471769

Journal Information:: Frontiers in Bioengineering and Biotechnology, Journal Name: Frontiers in Bioengineering and Biotechnology Vol. 10; ISSN 2296-4185

Publisher:: Frontiers Media S.A.Copyright Statement

Country of Publication:: United States

Language:: English

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