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

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1,2, Farbod Farhangi1

  • 1Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran.

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|September 28, 2021
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Summary

Machine learning identified high-risk COVID-19 areas in Tehran based on land use. Public transport stations and pharmacies were most correlated with disease spread, indicating urban planning

Keywords:
COVID-19 crisisdata-driven algorithmsgeographic information system (GIS)health geographyspatial modeling

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Area of Science:

  • Epidemiology
  • Urban Planning
  • Data Science

Background:

  • Population concentration in urban land uses can facilitate COVID-19 transmission.
  • Understanding the relationship between socio-economic land use and disease spread is crucial for public health interventions.

Purpose of the Study:

  • To create a COVID-19 risk map for Tehran, Iran.
  • To analyze the influence of socio-economic land use criteria on disease prevalence using machine learning.

Main Methods:

  • A spatial database of 2282 COVID-19 cases and eight socio-economic land uses was compiled.
  • Three machine learning algorithms (Random Forest, ANFIS, Logistic Regression) were employed for risk modeling.
  • Feature selection (OneR) and correlation analysis (Pearson) were conducted.

Main Results:

  • Random Forest achieved the highest accuracy (AUC=0.803) in predicting COVID-19 risk.
  • Central and eastern Tehran were identified as high-risk zones.
  • Public transportation stations and pharmacies showed the strongest correlation with COVID-19 case locations.

Conclusions:

  • Machine learning models effectively map COVID-19 risk based on urban land use.
  • The density and distribution of public transport stations, pharmacies, and banks are significant factors in COVID-19 prevalence in Tehran.
  • Findings can inform targeted public health strategies and urban planning to mitigate disease spread.