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

Published online by Cambridge University Press:  30 October 2024

Hassan Farhat Open the ORCID record for Hassan Farhat [Opens in a new window] ,
Guillaume Alinier ,
Nidaa Bajow Open the ORCID record for Nidaa Bajow [Opens in a new window] ,
Alan Batt ,
Mariana Charbel Helou Open the ORCID record for Mariana Charbel Helou [Opens in a new window] ,
Craig Campbell ,
Heejun Shin Open the ORCID record for Heejun Shin [Opens in a new window] ,
Luc Mortelmans Open the ORCID record for Luc Mortelmans [Opens in a new window] ,
Arezoo Dehghani Open the ORCID record for Arezoo Dehghani [Opens in a new window]  and
Carolyn Dumbeck
...Show all authors
Hassan Farhat*
Affiliation:
Ambulance Service, Hamad Medical Corporation, Doha, Qatar Faculty of Medicine “Ibn El Jazzar,” University of Sousse, Sousse, Tunisia Faculty of Sciences, University of Sfax, Sfax, Tunisia
Guillaume Alinier
Affiliation:
Ambulance Service, Hamad Medical Corporation, Doha, Qatar School of Health and Social Work, University of Hertfordshire, Hatfield, UK Weill Cornell Medicine-Qatar, Doha, Qatar Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
Nidaa Bajow
Affiliation:
Security Forces Hospital, Riyadh, Saudi Arabia
Alan Batt
Affiliation:
Queen’s University, Ontario, Canada Monash University, Melbourne, Australia
Mariana Charbel Helou
Affiliation:
School of Medicine, Lebanese American University, Beirut, Lebanon Lebanese American University-Rizk Hospital, Beirut, Lebanon
Craig Campbell
Affiliation:
School of Paramedicine, University of Tasmania, Tasmania, Australia
Heejun Shin
Affiliation:
Soonchunhyang Disaster Medicine Center, Seoul, South Korea Soonchunhyang University Bucheon Hospital, Seoul, South Korea Shin’s Disaster Medicine Academy, Seoul, South Korea Harvard Medical School, Harvard University, Boston, MA, USA
Luc Mortelmans
Affiliation:
European Society for Emergency Medicine, Antwerp, Belgium Catholic University of Leuven, Leuven, Belgium Free University Brussels, Brussels, Belgium
Arezoo Dehghani
Affiliation:
Safety Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Health in Disasters and Emergencies Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
Carolyn Dumbeck
Affiliation:
Alberta Health Services, Alberta, Canada
Roberto Mugavero
Affiliation:
University of Rome “Tor Vergata,” Department of Electronic Engineering, Rome, Italy University of the Republic of San Marino, Centre for Security Studies Observatory on Security and CBRNe Defense, Rome, Italy
Walid Abougalala
Affiliation:
SAMU 01 North East, Ministry of Health, Tunis, Tunisia
Saida Zelfani
Affiliation:
SAMU 01 North East, Ministry of Health, Tunis, Tunisia Faculty of Medicine, University of Tunis El Manar, Tunis, Tunisia
James Laughton
Affiliation:
Ambulance Service, Hamad Medical Corporation, Doha, Qatar
Gregory Ciottone
Affiliation:
Harvard Medical School, Harvard University, Boston, MA, USA Harvard T.H. Chan School of Public Health, Boston, MA, USA
Mohamed Ben Dhiab
Affiliation:
Ambulance Service, Hamad Medical Corporation, Doha, Qatar
*
Corresponding author: Hassan Farhat, PhD Email: [email protected]
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Abstract

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.

Keywords

preparednessresponsedisaster managementartificial intelligenceCBRN
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 18 , 2024 , e244
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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