COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms

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.

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

“…Distance to the Xinfadi market was the most dominant as this market was known as one of the epicenters of the outbreak. The positive effect of distance to hospitals, public transportation stations, pharmacies, and large supermarkets on COVID-19 cases was also reported for cases in London and Tehran ( Razavi-Termeh et al, 2021 ; Sun et al, 2021 ). You et al (2020) discussed that as large retail stores are hot spots of COVID19 cases, the distribution of small retail stores through residential areas could mitigate this effect.…”

“…Walkable and physically well-designed sidewalks are associated with a lower number of cases on the neighborhood scale ( Kwok et al, 2021 ; Credit, 2020 ; Tribby and Hartmann, 2021 ; Nguyen et al, 2020 ). However, urban districts with a higher density of transportation facilities such as bus and train stations increased the likelihood of COVID-19 spreading in Tehran ( Razavi-Termeh et al, 2021 ; Khavarian-Garmsir et al, 2021 ), Hong Kong ( Kan et al, 2021 ; Huang et al, 2020 ) Wuhan ( Xu et al, 2022 ; Niu et al, 2021 ) and Huangzhou ( B. Li et al, 2021 ).…”

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

“…In these knowledge-based approaches, there is a subjective influence on the relative importance of factors. In contrast, data-driven approaches based on multivariate models enable parametrizations that are based on the sensitive analysis of factors without the impact of subjectivity [70].…”

Spatial prediction of physical and chemical properties of soil using optical satellite imagery: a state-of-the-art hybridization of deep learning algorithm

“…MSE and RMSE evaluate a model's ability to predict data. The lower values of MSE and RMSE indicate higher modeling accuracy (Razavi-Termeh et al, 2021).…”