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Epidemiol Health System J. 2025;12(2): 73-79.
doi: 10.34172/ehsj.26450
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Original Article

Using Geographic Information System for Geospatial Analysis of the Measles Epidemic in Qom Province in 2024

Naser Rajabi 1 ORCID logo, Abedin Saghafipour 1 ORCID logo, Seyed Mohsen Zahraei 2 ORCID logo, Azam Sabouri 2 ORCID logo, Reza Nafarshalamzari 3 ORCID logo, Mahsa Sarvi 4* ORCID logo, Alireza Omidi Oskouei 1 ORCID logo

1 Department of Public Health, Faculty of Health, Qom University of Medical Sciences, Qom, Iran
2 Center for Communicable Disease Control, Ministry of Health and Medical Education, Tehran, Iran
3 Qom Provincial Health Center, Qom University of Medical Sciences, Qom, Iran
4 Department of Public Health, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran
*Corresponding Author: Mahsa Sarvi, Email: mahsasarvy@gmail.com

Abstract

Background and aims: Measles is a highly contagious viral disease that can affect susceptible individuals with a transmission probability of over 90% through close contact. Despite the availability of an effective and safe vaccine, measles remains a significant cause of morbidity and mortality among young children worldwide. This study aims to analyze the geospatial distribution of the measles epidemic using Geographic Information System (GIS) technology in Qom Province in 2024.

Methods: This cross-sectional descriptive-analytical study utilized data from all clinically and laboratory-confirmed measles cases in Qom Province registered on the Ministry of Health portal. A total of 129 cases were analysed using ESRI ArcGIS 10.8.2 to map and model the spatial distribution of measles cases. Demographic data, including age, gender, nationality, and residence, were collected and analysed using SPSS software.

Results: Among 129 suspected measles cases, 72 (55.8%) were males, and 86.04% lived in urban areas. Laboratory tests confirmed 16 (12.4%) measles-positive cases, of which 11 (68.75%) were unvaccinated and more than half (65.1%) were in the age group under 5 years. Spatial analysis showed significant clustering (z-score=1.69) in Districts 3 (Imam Khomeini Street) and 6 (Imamzadeh Ebrahim Street) of Qom city, which were identified as high-risk areas.

Conclusion: GIS technology highlighted spatial clusters of measles in Qom’s central urban areas, enabling real-time mapping and hotspot detection for continuous monitoring. These findings underscore the importance of targeted vaccination campaigns and continuous monitoring using GIS analyses to prevent future outbreaks.


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Submitted: 22 May 2025
Revision: 27 Jul 2025
Accepted: 28 Jul 2025
ePublished: 02 Dec 2025
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