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

Author

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  • Seyed Vahid Razavi-Termeh

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran)

  • Abolghasem Sadeghi-Niaraki

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran
    Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea)

  • Farbod Farhangi

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran)

  • Soo-Mi Choi

    (Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea)

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.

Suggested Citation

  • Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Farbod Farhangi & Soo-Mi Choi, 2021. "COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(18), pages 1-21, September.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9657-:d:634824
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    Cited by:

    1. Fatemeh Sadat Hosseini & Myoung Bae Seo & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Mohammad Jamshidi & Soo-Mi Choi, 2023. "Geospatial Artificial Intelligence (GeoAI) and Satellite Imagery Fusion for Soil Physical Property Predicting," Sustainability, MDPI, vol. 15(19), pages 1-25, September.
    2. Mohammad Tabasi & Ali Asghar Alesheikh & Mohsen Kalantari & Abolfazl Mollalo & Javad Hatamiafkoueieh, 2023. "Spatio-Temporal Modeling of COVID-19 Spread in Relation to Urban Land Uses: An Agent-Based Approach," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    3. Mahsa Farahani & Seyed Vahid Razavi-Termeh & Abolghasem Sadeghi-Niaraki & Soo-Mi Choi, 2023. "A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes," Sustainability, MDPI, vol. 15(13), pages 1-30, July.

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