TY - JOUR
T1 - A review about COVID-19 in the MENA region
T2 - environmental concerns and machine learning applications
AU - Meskher, Hicham
AU - Belhaouari, Samir Brahim
AU - Thakur, Amrit Kumar
AU - Sathyamurthy, Ravishankar
AU - Singh, Punit
AU - Khelfaoui, Issam
AU - Saidur, Rahman
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/11
Y1 - 2022/11
N2 - Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination. Graphical Abstract: [Figure not available: see fulltext.]
AB - Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination. Graphical Abstract: [Figure not available: see fulltext.]
KW - Artificial intelligent
KW - COVID-19
KW - Environmental analysis
KW - MENA
KW - Machine learning
KW - Meteorological factors
UR - http://www.scopus.com/inward/record.url?scp=85139674391&partnerID=8YFLogxK
U2 - 10.1007/s11356-022-23392-z
DO - 10.1007/s11356-022-23392-z
M3 - Review article
C2 - 36223015
AN - SCOPUS:85139674391
SN - 0944-1344
VL - 29
SP - 82709
EP - 82728
JO - Environmental Science and Pollution Research
JF - Environmental Science and Pollution Research
IS - 55
ER -