Effect of Environmental Factors on the Spread of COVID-۱۹ in the Neighborhoods of Qom Metropolis

Background & Aims: The prevalence of diseases such as COVID-۱۹ has significantly impacted citizens' lives. Understanding the dynamics and triggering factors of disease on a small spatial scale leads to the formulation of strategies that ultimately result in disease control and reduction. Materials and Methods: This applied, descriptive-analytical research utilizes spatial analysis and Geographic Information Systems (GIS) to examine the influence of environmental components on the prevalence of disease in the neighborhoods of the metropolitan area of Qom. The statistical population comprises individuals infected with COVID-۱۹ from January ۲۰۲۰ to September ۲۰۲۱ (۳۳,۰۰۰ people) across ۱۳۸ neighborhoods in Qom city. Hotspot analysis was employed to identify spatial patterns, and regression models were used to examine the impact of various indicators. Results: The results revealed that the spatial pattern of disease in Qom neighborhoods was not uniform, with hotspots of disease prevalence located in affluent neighborhoods and hotspots of mortality found in less affluent areas. Additionally, ۴۰% of COVID-۱۹ prevalence was influenced by three indicators: the rate of students, the rate of individuals with over ten years of unemployment, and the mixed residential-commercial per capita in urban neighborhoods. Conclusion: Based on the research findings, it has been determined that COVID-۱۹ prevalence in the neighborhoods of Qom City was not uniform, with hotspots of disease in neighborhoods such as Amin Boulevarden, Saheli, Mosalla, Zeynabiyeh, and Jahān Bini, located in the southwestern part of Qom city. Furthermore, assessing the impact of social, economic, physical, and demographic indicators on COVID-۱۹ prevalence in Qom neighborhoods using Geographic Weighted Regression and Ordinary Least Squares regression showed that three variables—the mixed residential-commercial per capita land use, the rate of students, and the rate of unemployed individuals—accounted for ۴۰% of COVID-۱۹ prevalence in these neighborhoods. In fact, analyzing the effective factors in the spread of COVID-۱۹ in the neighborhoods of Qom provided comprehensive insights that could be considered for preventive measures.

کلیدواژه ها:

COVID-۱۹ ، Geographic Information Systems (GIS) ، Iran ، Qom city ، Regression analysis ، Spatial analysis