Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives
Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strateg...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Microorganisms |
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| Online Access: | https://www.mdpi.com/2076-2607/13/5/1169 |
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| author | Weihe Zhu Daxi Wang Pengsong Li Haohao Deng Ziqing Deng |
| author_facet | Weihe Zhu Daxi Wang Pengsong Li Haohao Deng Ziqing Deng |
| author_sort | Weihe Zhu |
| collection | DOAJ |
| description | Wastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial–temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management. |
| format | Article |
| id | doaj-art-487c0ac0ac454002a370ab7e4cc146fd |
| institution | OA Journals |
| issn | 2076-2607 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Microorganisms |
| spelling | doaj-art-487c0ac0ac454002a370ab7e4cc146fd2025-08-20T01:56:41ZengMDPI AGMicroorganisms2076-26072025-05-01135116910.3390/microorganisms13051169Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future PerspectivesWeihe Zhu0Daxi Wang1Pengsong Li2Haohao Deng3Ziqing Deng4Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, ChinaBGI Research, Beijing 102601, ChinaBeijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing 100083, ChinaBGI Research, Beijing 102601, ChinaBGI Research, Beijing 102601, ChinaWastewater-based epidemiology (WBE) has emerged as a transformative approach for community-level health monitoring, particularly during the COVID-19 pandemic. This review critically examines the methodological framework of WBE systems through the following three core components: (1) sampling strategies that address spatial–temporal variability in wastewater systems, (2) comparative performance of different platforms in pathogen detection, and (3) predictive modeling integrating machine learning approaches. We systematically analyze how these components collectively overcome the limitations of conventional surveillance methods through early outbreak detection, asymptomatic case identification, and population-level trend monitoring. While highlighting technical breakthroughs in viral concentration methods and variant tracking through sequencing, the review also identifies persistent challenges, including data standardization, cost-effectiveness concerns in resource-limited settings, and ethical considerations in public health surveillance. Drawing insights from global implementation cases, we propose recommendations for optimizing each operational phase and discuss emerging applications beyond pandemic response. This review highlights WBE as an indispensable tool for modern public health, whose methodological refinements and cross-disciplinary integration are critical for transforming pandemic surveillance from reactive containment to proactive population health management.https://www.mdpi.com/2076-2607/13/5/1169wastewater-based epidemiologypublic health surveillancesamplingpathogen detectionepidemic forecast modeling |
| spellingShingle | Weihe Zhu Daxi Wang Pengsong Li Haohao Deng Ziqing Deng Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives Microorganisms wastewater-based epidemiology public health surveillance sampling pathogen detection epidemic forecast modeling |
| title | Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives |
| title_full | Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives |
| title_fullStr | Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives |
| title_full_unstemmed | Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives |
| title_short | Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives |
| title_sort | advances in wastewater based epidemiology for pandemic surveillance methodological frameworks and future perspectives |
| topic | wastewater-based epidemiology public health surveillance sampling pathogen detection epidemic forecast modeling |
| url | https://www.mdpi.com/2076-2607/13/5/1169 |
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