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

Datos Bibliográficos

ID15501201
AutoresSina Shaffiee Haghshenas (0000-0003-2859-3920, University of Calabria), Behrouz Pirouz (0000-0002-9163-3393, University of Calabria), Sami Shaffiee Haghshenas (0000-0002-9301-8677, University of Calabria), Behzad Pirouz (0000-0003-4156-4454, University of Calabria), Patrizia Piro (0000-0002-9202-6544, University of Calabria), Kyoung‐Sae (0000-0002-0148-9827, Gachon University Gil Medical Center), Seo‐Eun Cho (0000-0002-3991-2192, Gachon University Gil Medical Center), Zong Geem (0000-0002-0370-5562, Gachon University, autor de correspondencia)
Año2020
Volumen17
Número10
Páginas3730-3730
Fecha de publicación2020-05-25
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores de la revistaISSN: 1661-7827 • E-ISSN: 1660-4601
EditorialMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17103730
PMID32466199
OpenAlexW3028409905
IdiomaEN
Citas recibidas4
Referencias citadas8

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

Artificial neural network · Geography · Machine learning · Meteorology · Particle swarm optimization · Population · Process (computing · Wind speed · Computer Science · COVID-19 diagnosis using AI · COVID-19 epidemiological studies · COVID-19 impact on air quality