Preparedness and Response Strategies for Chemical, Biological, Radiological, and Nuclear Incidents in the Middle East and North Africa: An Artificial Intelligence-Enhanced Delphi Approach

  • Farhat, Hassan; 
  • Alinier, Guillaume; 
  • Bajow, Nidaa; 
  • Batt, Alan; 
  • Helou, Mariana Charbel; 
  • ... Shin, Heejun; 
  • 외 10명
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초록

Objective: Chemical, biological, radiological, and nuclear (CBRN) incidents require meticulous preparedness, particularly in the Middle East and North Africa (MENA) region. This study evaluated CBRN response operational flowcharts, tabletop training scenarios methods, and a health sector preparedness assessment tool specific to the MENA region. Methods: An online Delphi survey engaging international disaster medicine experts was conducted. Content validity indices (CVIs) were used to validate the items. Consensus metrics, including interquartile ranges (IQRs) and Kendall's W coefficient, were utilized to assess the panelists' agreement levels. Advanced artificial intelligence computing methods, including sentiment analysis and machine-learning methods (t-distributed stochastic neighbor embedding [t-SNE] and k-means), were used to cluster the consensus data. Results: Forty experts participated in this study. The item-level CVIs for the CBRN response flowcharts, preparedness assessment tool, and tabletop scenarios were 0.96, 0.85, and 0.84, respectively, indicating strong content validity. Consensus analysis demonstrated an IQR of 0 for most items and a strong Kendall's W coefficient, indicating a high level of agreement among the panelists. The t-SNE and k-means identified four clusters with greater European response engagement. Conclusions: This study validated essential CBRN preparedness and response tools using broad expert consensus, demonstrating their applicability across different geographic areas.

키워드

preparedness; response; disaster management; artificial intelligence; CBRN
제목
Preparedness and Response Strategies for Chemical, Biological, Radiological, and Nuclear Incidents in the Middle East and North Africa: An Artificial Intelligence-Enhanced Delphi Approach
저자
Farhat, Hassan; Alinier, Guillaume; Bajow, Nidaa; Batt, Alan; Helou, Mariana Charbel; Campbell, Craig; Shin, Heejun; Mortelmans, Luc; Dehghani, Arezoo; Dumbeck, Carolyn; Mugavero, Roberto; Abougalala, Walid; Zelfani, Saida; Laughton, James; Ciottone, Gregory; Dhiab, Mohamed Ben
DOI
10.1017/dmp.2024.160
발행일
2024-10
유형
Article
저널명
Disaster Medicine and Public Health Preparedness
권
18