ENHANCING HEALTH MONITORING SYSTEMS DURING HAJJ 2024 USING OPEN SOURCE EPIDEMIC INTELLIGENCE

Background: The annual Hajj pilgrimage in Mecca, Saudi Arabia, attracts over two million people, creating a high-risk environment for public health emergencies, including infectious disease outbreaks and heat-related illnesses due to extreme temperatures and mass gatherings. Traditional surveillance systems can be challenged by the event's scale and speed. This study evaluates the integration of Open Source Epidemic Intelligence (EIOS) to enhance real-time health monitoring and response during Hajj 2024. Methods: A cross-sectional study was conducted using the EIOS platform, which aggregates and analyzes data from digital sources like news and social media. During the Hajj period (June 2-21, 2024), 877,000 articles were screened. A multi-step process involving screening, triage based on predefined public health criteria, and risk assessment by experts was used to identify and classify relevant health signals. Results: From the initial 877,000 articles, 96 unique health cases were identified. Heat-related illnesses were the most prevalent, with Heat Stroke (18 cases) and Heat Stress (15 cases) being the top concerns. Infectious diseases like Cholera (15 cases) and Dengue (12 cases) were primarily detected outside Saudi Arabia before Hajj. Statistical analysis revealed a significant increase in health incidents during Hajj (p<0.05), with 33.3% (n=32) of cases classified as moderate-to-high risk, predominantly heat-related events occurring inside Saudi Arabia. Conclusion: The EIOS system proved effective for real-time public health surveillance during Hajj 2024, successfully identifying and prioritizing critical health threats, particularly heat-related illnesses. The findings underscore the value of EIOS as an early warning tool for mass gatherings. Future implementations should focus on integrating predictive AI modeling, automating data processing, and combining EIOS with traditional health data to enable more proactive and targeted public health interventions.

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Publication Details

Journal
Lex localis - Journal of Local Self-Government
Published
2025-08-25
DOI
https://doi.org/10.52152/801173
Citations
1
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
1.35
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article

ENHANCING HEALTH MONITORING SYSTEMS DURING HAJJ 2024 USING OPEN SOURCE EPIDEMIC INTELLIGENCE

Ghadi Subahi, Abdullah M. Assiri, Ahmad Alkhorisi, Khalid Almutairi et al.
1 citations
Lex localis - Journal of Local Self-Government
Data-Driven Disease Surveillance
1.35
article

ENHANCING HEALTH MONITORING SYSTEMS DURING HAJJ 2024 USING OPEN SOURCE EPIDEMIC INTELLIGENCE

Ghadi Subahi, Abdullah M. Assiri, Ahmad Alkhorisi, Khalid Almutairi, Ahmed Mokharshum, Haya AlHagbani, Ahmed AlSallum
article en
1 citations

Abstract

Background: The annual Hajj pilgrimage in Mecca, Saudi Arabia, attracts over two million people, creating a high-risk environment for public health emergencies, including infectious disease outbreaks and heat-related illnesses due to extreme temperatures and mass gatherings. Traditional surveillance systems can be challenged by the event's scale and speed. This study evaluates the integration of Open Source Epidemic Intelligence (EIOS) to enhance real-time health monitoring and response during Hajj 2024. Methods: A cross-sectional study was conducted using the EIOS platform, which aggregates and analyzes data from digital sources like news and social media. During the Hajj period (June 2-21, 2024), 877,000 articles were screened. A multi-step process involving screening, triage based on predefined public health criteria, and risk assessment by experts was used to identify and classify relevant health signals. Results: From the initial 877,000 articles, 96 unique health cases were identified. Heat-related illnesses were the most prevalent, with Heat Stroke (18 cases) and Heat Stress (15 cases) being the top concerns. Infectious diseases like Cholera (15 cases) and Dengue (12 cases) were primarily detected outside Saudi Arabia before Hajj. Statistical analysis revealed a significant increase in health incidents during Hajj (p<0.05), with 33.3% (n=32) of cases classified as moderate-to-high risk, predominantly heat-related events occurring inside Saudi Arabia. Conclusion: The EIOS system proved effective for real-time public health surveillance during Hajj 2024, successfully identifying and prioritizing critical health threats, particularly heat-related illnesses. The findings underscore the value of EIOS as an early warning tool for mass gatherings. Future implementations should focus on integrating predictive AI modeling, automating data processing, and combining EIOS with traditional health data to enable more proactive and targeted public health interventions.

Lex localis - Journal of Local Self-GovernmentVol. 23(S4)
Openalex Percentile: Top 15%
Data-Driven Disease Surveillance
1.35
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