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Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Author

Listed:
  • Sina Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Behrouz Pirouz

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Sami Shaffiee Haghshenas

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Behzad Pirouz

    (Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy)

  • Patrizia Piro

    (Department of Civil Engineering, University of Calabria, 87036 Rende, Italy)

  • Kyoung-Sae Na

    (Gil Medical Center, Gachon University, Incheon 21565, Korea)

  • Seo-Eun Cho

    (Gil Medical Center, Gachon University, Incheon 21565, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

Abstract

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Suggested Citation

  • Sina Shaffiee Haghshenas & Behrouz Pirouz & Sami Shaffiee Haghshenas & Behzad Pirouz & Patrizia Piro & Kyoung-Sae Na & Seo-Eun Cho & Zong Woo Geem, 2020. "Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications," IJERPH, MDPI, vol. 17(10), pages 1-21, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3730-:d:362633
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    References listed on IDEAS

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    1. Behrouz Pirouz & Natale Arcuri & Behzad Pirouz & Stefania Anna Palermo & Michele Turco & Mario Maiolo, 2020. "Development of an Assessment Method for Evaluation of Sustainable Factories," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
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    3. Diego Galvan & Luciane Effting & Hágata Cremasco & Carlos Adam Conte-Junior, 2020. "Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    4. Sachiko Kodera & Essam A. Rashed & Akimasa Hirata, 2020. "Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity," IJERPH, MDPI, vol. 17(15), pages 1-14, July.

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