Artificial Intelligence in Mass Gathering Health from the Perspective of Public Health Framework for the Arbaeen Pilgrimage: A Scoping Review
| Author | Ebrahim Babaee | en |
| Author | Nahid Nafissi | en |
| Author | Laya Fathollahi | en |
| Author | Nasibeh Rady Raz | en |
| Orcid | Ebrahim Babaee [0000-0001-7969-9122] | en |
| Orcid | Nahid Nafissi [0000-0001-7454-9985] | en |
| Orcid | Laya Fathollahi [0000-0003-1039-1589] | en |
| Orcid | Nasibeh Rady Raz [0000-0003-1039-1589] | en |
| Issued Date | 2026-10-31 | en |
| Abstract | Context: The Arbaeen pilgrimage, recognized as one of the world’s largest annual religious mass gatherings, presents unique operational and epidemiological complexities that require advanced surveillance and response capacities. This study aimed to examine current applications of artificial intelligence (AI) in mass-gathering health management and to propose an integrated AI-enabled public health framework tailored to the Arbaeen context. Evidence Acquisition: A structured scoping review was conducted in 2026 at the Iran University of Medical Sciences, Tehran, Iran. Relevant literature was identified through searches of PubMed, Scopus, Web of Science, and Google Scholar using combinations of terms related to artificial intelligence, machine learning, crowd surveillance, epidemiology, emergency preparedness, and mass gatherings. Twenty studies meeting the inclusion criteria were analyzed using thematic and function-level synthesis. Results: The results indicate that AI applications are primarily used for crowd surveillance, density estimation, anomaly detection, predictive modeling, and optimizing emergency medical services. Deep learning approaches, particularly convolutional neural networks (CNNs), graph-based models, Long Short-Term Memory (LSTM) networks, and machine learning forecasting algorithms, are the most commonly applied techniques. Key structural gaps include fragmentation across surveillance, prediction, and response systems; limited operational integration; and insufficient adaptation to prolonged religious pilgrimages such as Arbaeen. Conclusions: Based on these findings, we propose an integrated AI-enabled framework for Arbaeen to enable real-time multimodal surveillance, predictive public health intelligence, adaptive response systems, and end-to-end operational integration. The proposed model emphasizes continuous situational awareness, predictive risk assessment, and coordinated emergency response to support resilient public health management during large-scale religious gatherings. | en |
| DOI | https://doi.org/10.69107/mgmj-173086 | en |
| URI | https://brieflands.com/journals/mgmj/articles/173086 | en |
| Keyword | Artificial Intelligence | en |
| Keyword | Mass Gatherings | en |
| Keyword | Arbaeen Pilgrimage | en |
| Keyword | Public Health Surveillance | en |
| Keyword | Emergency Preparedness | en |
| Publisher | Brieflands | en |
| Title | Artificial Intelligence in Mass Gathering Health from the Perspective of Public Health Framework for the Arbaeen Pilgrimage: A Scoping Review | en |
| Type | Review Article | en |
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