Spatio-temporal analysis of COVID-19 incidence rate using GIS: a case study—Tehran metropolitan, Iran

Abstract

COVID-19 has been distinguished as a zoonotic coronavirus, like SARS coronavirus and MERS coronavirus. Tehran metropolis, as the capital of Iran, has a high density of residents that experienced a high incidence and mortality rates which daily increase the number of death and cases. In this study, the IDW (Inverse Distance Weight), Hotspots, and GWR (Geography Weighted Regression) Model are used as methods for analyzing big data COVID-19 in Tehran. The results showed that the majority of patients and deaths were men, but the death rate was higher in women than in men; also was observed a direct relationship between the area of the houses, and the infected rate, to COVID-19. Also, the results showed a disproportionate distribution of patients in Tehran, although in the eastern regions the number of infected people is higher than in other districts; the eastern areas have a high population density as well as residential land use, and there is a high relationship between population density in residential districts and administrative-commercial and the number of COVID-19 cases in all regions. The outputs of local R 2 were interesting among patients and underlying disorders; the local R 2 between hypertension and neurological diseases was 0.91 and 0.79, respectively, which was higher than other disorders. The highest rates of local R 2 for diabetes and heart disease were 0.67 and 0.55, respectively. From this study, it can be concluded the restrictions must be considered especially, in areas densely populated for all people.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

“…The impact of land use on the spread of COVID-19 is also measured by density and POIs. Higher density of specific land uses such as supermarkets, commercial land uses, clinics, hospitals, administrative, schools, shopping centers, and restaurants are found as the positive and significant predictors of COVID-19 cases in Tehran, Hong Kong, New York, Chicago, Wuhan and Huangzhou ( Ma et al, 2021 ; Nasiri et al, 2021 ; Li et al, 2020 ; Lak et al, 2021 ; Huang et al, 2021 , Huang et al, 2020 ; Yip et al, 2021 ). The high density of these land uses will contribute to higher POI and accordingly a higher rate of COVID-19 cases ( Xu et al, 2022 ; B. Li et al, 2021 ).…”

“…Around 30 % of the reviewed papers are conducted on urban district or neighborhood scales. While about 56 % of these papers found density as a positive and significant predictor of COVID-19 cases, this is the lowest among different scales ( Verma et al, 2021 ; Hao et al, 2020 ; Nasiri et al, 2021 ; Han and Jia, 2021 ; Xu et al, 2022 ). However, about 13 % of papers found that areas with higher population density have a lower infection rate in cities like New York ( Tribby and Hartmann, 2021 ; Credit, 2020 ), Hong Kong ( Huang et al, 2020 ), London ( Sun et al, 2021 ) and Fortaleza ( Cestari et al, 2021 ).…”

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

“…Oluyomi et al [ 151 ] implemented the area interpolation tool in ArcGIS Pro 2.6 using a k-Bessel model to visualize and obtain predicted values of COVID-19 incidence at the census tract level. While Nasiri et al [ 107 ] used the IDW method to create interpolated maps of infected COVID-19 patients across Tehran, Iran. Ramírez and Li [ 164 ] used the IDW algorithm to interpolate and create a 3D continuous surface of hotspots of COVID-19 incidence across counties in the USA at five-time points.…”

Spatiotemporal pattern of Covid-19 outbreak in Turkey

“…Therefore, The World Health Organizaton has consistently used spatial data analysis to control infectious diseases (Esri, 2020;Nasiri et al, 2021). Within the scope of the research of measures that can be taken to prevent the pandemic, many researchers in different countries have revealed the effects of spatial factors on the fast spread of coronavirus (Adegboye et al, 2021;Casado-Aranda et al, 2021;Castro et al, 2021;Gupta et al, 2021;Li et al, 2021;Liu et al, 2021;Maiti et al, 2021;Mansour et al, 2021;Nasiri et al, 2021;Rubino et al, 2020;Sarkar et al, 2021;Shariati et al, 2020;Tang et al, 2020;Tao et al, 2020;Vaz, 2021;Xiong et al, 2020). Ramírez-Aldana et al (2020) determined that the number of Covid-19 cases in Iranian provinces is spatially related.…”