TY - JOUR
T1 - Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques : A comprehensive review and outlook
AU - Abdeldayem, Omar M.
AU - Dabbish, Areeg M.
AU - Habashy, Mahmoud M.
AU - Mostafa, Mohamed K.
AU - Elhefnawy, Mohamed
AU - Amin, Lobna
AU - Al-Sakkari, Eslam G.
AU - Ragab, Ahmed
AU - Rene, Eldon R.
N1 - Funding Information:
The authors dedicate this paper to the memorial of their colleague Omar Eldaghar who passed away in Uganda while serving the underprivileged communities to have access to basic human needs. We hope that we can carry on with his legacy and ethos in making this world a better place for everyone ?Shoulder to shoulder for those who lack basic human needs.?. E.R. Rene thanks IHE Delft for providing staff time support under the project ?Support to Society? to co-work with researchers from Egypt and students of the ERASMUS+ IMETE programme.
Publisher Copyright:
© 2021
PY - 2022/1/10
Y1 - 2022/1/10
N2 - A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.
AB - A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.
KW - Artificial intelligence
KW - Artificial neural networks
KW - COVID-19
KW - Deep learning
KW - Machine learning
KW - Reinforcement Learning
KW - SARS-CoV-2
KW - Viral air surveillance
KW - Wastewater based-epidemiology
UR - http://www.scopus.com/inward/record.url?scp=85114771539&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2021.149834
DO - 10.1016/j.scitotenv.2021.149834
M3 - Article
AN - SCOPUS:85114771539
SN - 0048-9697
VL - 803
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 149834
ER -
