RESUMO
Background: Analyzing and monitoring the spatial-temporal patterns of the new coronavirus disease (COVID-19) pandemic can assist local authorities and researchers in detecting disease outbreaks in the early stages. Because of different socioeconomic profiles in Tehran's areas, we will provide a clear picture of the pandemic distribution in Tehran's neighbourhoods during the first months of its spread from February to July 2020, employing a spatial-temporal analysis applying the geographical information system (GIS). Disease rates were estimated by location during the 5 months, and hot spots and cold spots were highlighted. Methods: This study was performed using the COVID-19 incident cases and deaths recorded in the Medical Care Monitoring Centre from February 20, to July 20, 2020. The local Getis-Ord Gi* method was applied to identify the hotspots where the infectious disease distribution had significantly clustered spatially. A statistical analysis for incidence and mortality rates and hot spots was conducted using ArcGIS 10.7 software. Results: The addresses of 43,000 Tehrani patients (15,514 confirmed COVID-19 cases and 27,486 diagnosed as probable cases) were changed in its Geo-codes in the GIS. The highest incidence rate from February to July 2020 was 48 per 10,000 and the highest 5-month incidence rate belonged to central and eastern neighbourhoods. According to the Cumulative Population density of patients, the higher number is estimated by more than 2500 people in the area; however, the lower number is highlighted by about 500 people in the neighborhood. Also, the results from the local Getis-Ord Gi* method indicate that COVID-19 has formed a hotspot in the eastern, southeast, and central districts in Tehran since February. We also observed a death rate hot spot in eastern areas. Conclusion: Because of the spread of COVID-19 disease throughout Tehran's neighborhoods with different socioeconomic status, it seems essential to pay attention to health behaviors to prevent the next waves of the disease. The findings suggest that disease distribution has formed a hot spot in Tehran's eastern and central regions.
RESUMO
Since its emergence in late 2019, the COVID-19 pandemic has attracted the attention of researchers in various fields, including urban planning and design. However, the spreading patterns of the disease in cities are still not clear. Historically, preventing and controlling pandemics in cities has always been challenging due to various factors such as higher population density, higher mobility of people, and higher contact frequency. To shed more light on the spread patterns of the pandemic, in this study we analyze 43,000 confirmed COVID-19 cases at the neighborhood level in Tehran, the capital of Iran. To examine spatio-temporal patterns and place-based factors contributing to the spread of the pandemic, we used exploratory spatial data analysis and spatial regression. We developed a geo-referenced database composed of 12 quantitative place-based variables related to physical attributes, land use and public transportation facilities, and demographic status. We also used the geographically weighted regression model for the local examination of spatial non-stationarity. According to the results, population density (R2 = 0.88) and distribution of neighborhood centers (R2 = 0.59), drugstores (R2 = 0.64), and chain stores (R2 = 0.59) are the main factors contributing to the spread of the disease. Additionally, density of public transportation facilities showed a varying degree of contribution. Overall, our findings suggest that demographic composition and major neighborhood-level physical attributes are important factors explaining high rates of infection and mortality. Results contribute to gaining a better understanding of the role of place-based attributes that may contribute to the spread of the pandemic and can inform actions aimed at achieving Sustainable Development Goals, particularly Goals 3 and 11.