Infectious Disease Surveillance at Large Religious Mass Gatherings: A Scoping Review and Scenario-Based Modeling Study of the Hajj and Kumbh Mela (2010–2025)

Large religious mass gatherings create recurrent and predictable conditions for infectious disease transmission. These events combine extreme population density, prolonged duration, ritualized physical proximity, and large-scale domestic and international travel. Unlike concerts or sporting events, religious gatherings such as the Hajj and the Kumbh Mela cannot be freely rescheduled, as their timing is fixed by religious calendars and spiritual obligation. Public health systems must therefore adapt surveillance and response strategies to the event rather than eliminate the event itself. The Hajj in Saudi Arabia and the Kumbh Mela in India are the two largest recurring religious mass gatherings globally. Both have experienced repeated respiratory disease burdens, including influenza, other viral respiratory infections, and COVID-19. Both governments have invested in disease surveillance systems, digital reporting tools, outbreak detection mechanisms, and rapid response capacity, particularly during periods of heightened risk. Despite these investments, disease transmission remains common, and comparative synthesis of surveillance approaches across these two events remains limited. Existing literature often examines the Hajj or the Kumbh Mela in isolation, focuses on single outbreaks, or emphasizes epidemiologic outcomes without systematically mapping the surveillance architectures that structure early detection and response. This study treats infectious disease surveillance systems as public health infrastructure embedded within religious mass gatherings and examines how such systems are designed and operationalized over time. This study does not aim to identify best practices or evaluate effectiveness. It is descriptive and comparative. It maps surveillance system components and explores how different surveillance-informed assumptions may shape transmission dynamics through scenario-based modeling.