Geographical Distribution of COVID-19 Confirmed Cases in Iran: A Short Communication | ||
| Infection Epidemiology and Microbiology | ||
| Article 7, Volume 7, Issue 1, 2021, Pages 61-65 PDF (1.11 M) | ||
| Document Type: Brief Communication | ||
| DOI: 10.52547/iem.7.1.61 | ||
| Authors | ||
| Moslem Taheri Soodejani1; Hamid Reza Shoraka2; Seyyed Mohammad Tabatabaei* 3 | ||
| 1Department of Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, IR, Iran | ||
| 2Vector-Borne Diseases Research Center, North Khorasan University of Medical Sciences, Bojnurd, Iran | ||
| 3Medical Informatics Department, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran | ||
| Abstract | ||
| Background: In Iran, the first cases of SARS-CoV-2 disease were detected with the death of 2 people in Qom city. Then other cases were reported in Markazi, Tehran, and Gilan provinces, and after that the disease spread to all 31 provinces of the country. Materials and Methods: All data used in this study were collected from the reports of the National Committee on COVID-19 Epidemiology in the Ministry of Health and Medical Education in Iran. To investigate the effect of traveling between neighboring provinces, a spatial rate smoothing method was used, showing the impact of neighborhood on the disease prevalence. Also, to investigate the relationship between population density and disease prevalence, spatial regression was used at a significance level of 5%. Findings: Based on the estimated spatial rates, the disease prevalence rates changed in many provinces compared to the raw prevalence rates. Population density was also found to be directly related to the disease prevalence, so that with increasing population density, the disease prevalence rate increased (p <.001). Conclusion: It seems that case finding process should be done actively in all provinces of Iran regardless of administrative borders. Provinces should also be classified in terms of the disease transmission risk according to population density of patients, which may indicate the probability of contact between individuals. | ||
| Keywords | ||
| SARS-CoV-2; Coronavirus; Novel coronavirus; Spatial analysis | ||
| References | ||
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