Abstract
BackgroundUsing geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA).MethodsIn a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19th, 2020 to September 30th, 2020 were identified and included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in geographic information systems (GIS); in addition, the spatial autocorrelation of the coronavirus in different urban districts of the province was calculated using the LISA method.ResultsThe prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values.ConclusionsAccording to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.
Highlights
Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus
The epidemiological features of patients A total of 4,388 patients with COVID-19 were diagnosed from February 19th, 2020 to September 30th, 2020 in Qom Province located in central Iran, most of whom were natives and residents of urban areas of the province
The monthly incidence of COVID-19 disease in Qom Province shows that the frequency of cases increased from February to April, after the reduction of the disease in two months (Fig. 2)
Summary
Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. One of the main applications of epidemiology is to facilitate the identification of geographical data affected by diseases and vulnerable groups that are at a higher risk of controlling diseases and being expose to risk factors. The use of geographic information systems (GIS) to determine geographical distribution patterns of diseases in medical and health sciences has increased significantly [9,10,11,12]. Determining the geographical distribution of diseases, the spatial study of care facilities and health services, determining the geographical boundaries of communities that are essential components of epidemiological and health studies are some of the applications of GIS in the field of health [13]. Spatial modeling in GIS is directly used to understand the differences in the spatial distribution of diseases and their relationship with environmental factors and health care system; as a result, GIS technology is currently a major tool in health research in the field of infectious diseases [14]
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