ABSTRACT
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.
ABSTRACT
Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.
ABSTRACT
RATIONALE AND OBJECTIVES: Real-time polymerase chain reaction (RT-PCR) remains the gold standard for confirmation of Coronavirus Disease 2019 (COVID-19) despite having many disadvantages. Here, we investigated the diagnostic performance of chest computed tomography (CT) as an alternative to RT-PCR in patients with clinical suspicion of COVID-19 infection. METHODS: In this descriptive cross-sectional study, 27,824 patients with clinical suspicion of COVID-19 infection who underwent unenhanced low-dose chest CT from 20 February, 2020 to 21 May, 2020 were evaluated. Patients were recruited from seven specifically designated hospitals for patients with COVID-19 infection affiliated to Shahid Beheshti University of Medical Sciences. In each hospital, images were interpreted by two independent radiologists. CT findings were considered as positive/negative for COVID-19 infection based on RSNA diagnostic criteria. Then, the correlation between the number of daily positive chest CT scans and number of daily PCR-confirmed cases and COVID-19-related deaths in Tehran province during this three-month period was assessed. The trends of admission rate and patients with positive CT scans were also evaluated. RESULTS: A strong positive correlation between the numbers of daily positive CT scans and daily PCR-confirmed COVID-19 cases (râ¯=â¯0.913, p < 0.001) was observed. Furthermore, in hospitals located in regions with a lower socioeconomic status, the admission rate and number of positive cases within this three-month period was higher as compared to other hospitals. CONCLUSION: Low-dose chest CT is a safe, rapid and reliable alternative to RT-PCR for the diagnosis of COVID-19 in high-prevalence regions. In addition, our study provides further evidence for considering patients' socioeconomic status as an important risk factor for COVID-19.