Geographical Analysis of COVID-19 Epidemiology in Iran with Exploratory Spatial Data Analysis Approach (ESDA)

Document Type : Original Research

Authors

Department of Geography and Urban Planning, University Ferdowsi of Mashhad, Mashhad, Iran

Abstract

Background and Aim: The use of geophysical analysis of the epidemiology to identify geographical factors affecting the prevalence of the disease can be effective on community health policies to control the prevalence of the virus. Therefore, the present study is a geographical analysis of the COVID-19 epidemiology in Iran.
Therefore, the purpose of this study is the geographical analysis of coronavirus transmission in the country.
Methods: This is a descriptive-analytical study and ArcGIS and GeoDa software has been used to analyze the data. The statistical population included the total number of people infected with COVID-19 (n=21638) in Iran during February 22, 2020, and March 22, 2020. Data entered ArcGIS software by each province. In order to show the spatial distribution of COVID-19 patients in Iran, point density has been used based on the mentioned time period. Then, by using the Moran coefficient, its spatial distribution was examined. Also, by using spatial correlation, the distance between the spread of coronavirus in all provinces of Iran was analyzed. Finally, by using the local index of the single-variable Moran spatial bond, the spatial clustering of the country's provinces was calculated based on the coronavirus.
Results: Statistics show that the age group of 21-50 years is the highest percentage of people infected with COVID-19. The results showed that the most important factor in the spatial spread of coronavirus in Iran is the distance and proximity of the provinces affected by this disease so that at a distance of 383.8 km between the provinces, the Moran coefficient is 0.66627 and shows a positive spatial correlation. It is located at a distance of 762.6 km between the provinces and the Moran coefficient is -0.040246, which indicates a negative spatial correlation, which means that this distance decreases after the number of people with COVID-19. In spatial clustering, HH clusters including provinces (Tehran, Alborz, Qom, Mazandaran, Gilan, Qazvin, Isfahan, Semnan, Markazi and Yazd) are known as the main spatial propagation centers of the Coronavirus epidemic, which should be controlled and reduced. Also, LH clusters (including Golestan, Khorasan Razavi, North Khorasan, Ardabil and Hamedan provinces) are the ring around the center of damage, which should be controlled in terms of spatial interaction and proximity to HH clusters. Serious travel bans should be put in place to prevent the spread of coronavirus to the provinces in the LH cluster.
Conclusion: One of the most important geographical factors affecting the prevalence of coronavirus is based on spatial distribution theory, distance and spatial proximity. Officials and planners should intelligently reduce the number of people visiting offices and organizations, and by providing telecommuting, to prevent the upward trend of the outbreak of coronavirus in Iran.

Keywords

References

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Journal of Military Medicine
Volume 22, Issue 6 - Serial Number 91
July and August 2020
Pages 542-552

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