Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook
| Autor: | Eslam G. Al-Sakkari, Mahmoud M. Habashy, Areeg M. Dabbish, Ahmed Ragab, Lobna Amin, Eldon R. Rene, Mohamed Elhefnawy, Mohamed K. Mostafa, Omar M. Abdeldayem |
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| Rok vydání: | 2022 |
| Předmět: |
Wastewater-Based Epidemiological Monitoring
Artificial intelligence Air sampling Environmental Engineering Exploit Coronavirus disease 2019 (COVID-19) Computer science Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Wastewater based-epidemiology Wastewater Machine learning computer.software_genre Article Disease Outbreaks Pandemic Humans Environmental Chemistry Pandemics Waste Management and Disposal Artificial neural networks SARS-CoV-2 Transmission (medicine) business.industry COVID-19 Outbreak Deep learning Wastewater based epidemiology Pollution business computer Viral air surveillance |
| Zdroj: | The Science of the Total Environment |
| ISSN: | 0048-9697 |
| Popis: | 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 did not yet implement in a sufficient number of studies is the surveillance of 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 wastewater-based epidemiology, 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 the available data 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. Graphical abstract Unlabelled Image |
| Databáze: | OpenAIRE |
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