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Article

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

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Department of Civil Engineering, University of Calabria, 87036 Rende, Italy
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Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy
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Department of Computer Engineering, Modelling, Electronics and Systems Engineering, University of Calabria, 87036 Rende, Italy
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Gil Medical Center, Gachon University, Incheon 21565, Korea
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College of IT Convergence, Gachon University, Seongnam 13120, Korea
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(10), 3730; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103730
Received: 24 April 2020 / Revised: 19 May 2020 / Accepted: 21 May 2020 / Published: 25 May 2020
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. View Full-Text
Keywords: sustainable development; COVID-19; artificial intelligence; PSO; DE; feature selection sustainable development; COVID-19; artificial intelligence; PSO; DE; feature selection
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MDPI and ACS Style

Shaffiee Haghshenas, S.; Pirouz, B.; Shaffiee Haghshenas, S.; Pirouz, B.; Piro, P.; Na, K.-S.; Cho, S.-E.; Geem, Z.W. Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications. Int. J. Environ. Res. Public Health 2020, 17, 3730. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103730

AMA Style

Shaffiee Haghshenas S, Pirouz B, Shaffiee Haghshenas S, Pirouz B, Piro P, Na K-S, Cho S-E, Geem ZW. Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications. International Journal of Environmental Research and Public Health. 2020; 17(10):3730. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103730

Chicago/Turabian Style

Shaffiee Haghshenas, Sina, Behrouz Pirouz, Sami Shaffiee Haghshenas, Behzad Pirouz, Patrizia Piro, Kyoung-Sae Na, Seo-Eun Cho, and Zong W. Geem 2020. "Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications" International Journal of Environmental Research and Public Health 17, no. 10: 3730. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17103730

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