Abstract
The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease—public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.
Highlights
In December 2019, an acute respiratory syndrome was reported in Wuhan, HubeiProvince, China, due to the release of a new unknown virus called COVID-19
To the best of the authors’ knowledge, the impact of socio-economic land uses on the modeling of COVID-19 has not been used so far, and this study offers an approach to reduce population density in socio-economic land uses
This study examined an approach that combined machine learning, geographic information system (GIS), and urban land use to prepare a COVID-19 risk map
Summary
China, due to the release of a new unknown virus called COVID-19. New cases were identified all over China and around the world. COVID19 spreads relatively rapidly compared to SARS-CoV in 2002–2003 and MERS-CoV in. While the number of patients with MERS reached 1000 in about 30 months and the number of patients with SARS reached 1000 in approximately months, the number of COVID-19 patients reached 1000 in only 48 days [1]. COVID-19 spreads so fast that it was alarmingly declared a global epidemic by the World Health Organization (WHO). As of 4 May 2020, more than 3,435,894 people have been infected worldwide, and it can be concluded that COVID-19 has spread all around the world [3]
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