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Application of Multi-Criteria Decision Analysis Techniques for Informing Select Agent Designation and Decision
Segaran P Pillai1, Julia A Fruetel2, Kevin Anderson3
1Office of the Commissioner, Food and Drug Administration, U.S. Department of Health and Human Services, Silver Spring, MD, United States.
Frontiers in Bioengineering and Biotechnology
|June 20, 2022
Summary
Multi-criteria decision analysis (MCDA) and logic tree analysis can support the Centers for Disease Control and Prevention (CDC) Select Agent Program
Area of Science:
- Public Health
- Biosecurity
- Risk Assessment
Background:
- The Centers for Disease Control and Prevention (CDC) Select Agent Program regularly reviews biological agents and toxins that pose threats to public health and safety.
- Subject matter expert (SME) assessments are traditionally used to rank these agents during the biennial review process.
- Existing methods may benefit from enhanced analytical approaches for more robust agent evaluation.
Purpose of the Study:
- To explore the applicability of multi-criteria decision analysis (MCDA) and logic tree analysis for the CDC Select Agent Program's review process.
- To evaluate the generality of these analytical techniques by applying them broadly, including to non-select agents.
- To identify potential quantitative thresholds for classifying select agents and improve the accuracy of the review process.
Main Methods:
- Conducted a literature search for over 70 pathogens, assessing them against 15 criteria related to public health and bioterrorism risk.
- Applied multi-criteria decision analysis (MCDA) using a two-dimensional plot of weighted scores for "difficulty of attack" versus "consequences of an attack."
- Utilized logic tree analysis as a secondary approach to systematically exclude pathogens from consideration as select agents.
Main Results:
- Identified significant data gaps for aerosol stability and human infectious dose (inhalation/ingestion) across numerous pathogens.
- The MCDA approach, particularly the two-dimensional plot, provided better agent placement insights than simple ranking.
- Sensitivity analysis indicated that quantitative thresholds for classifying select agents are plausible, with good agreement with current designations, though some agents scored near potential thresholds.
Conclusions:
- MCDA and logic tree analysis are valuable tools to support the CDC Select Agent Program's biennial review, offering enhanced analytical rigor.
- Accurate SME assessments and addressing data gaps are critical for the reliable application of these analytical methods.
- The findings suggest that quantitative thresholds are feasible for select agent classification, improving upon subjective "by eye" assessments.
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