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Wastewater-based epidemiology (WBE) is a cost-effective and rapid survey approach that involves sampling and detecting chemical and biological markers in wastewater. WBE has been primarily employed to reveal information about illicit drug and pharmaceutical usage, diet, lifestyle, community health, and infectious diseases. In recent years, there has been a surge in research and implementation of WBE to detect COVID-19, which is critical for early-warning and tracking community transmission. Compared to other types of epidemiological surveillance, WBE offers many advantages but also has inherent methodological limitations and associated uncertainties. This talk will address various WBE uncertainties related to biomarker shedding, in-sewer decay and transport, wastewater sampling, biomarker analysis, and the back-calculation method. Comprehensive studies, using approaches such as systematic reviews, bioreactor tests, field studies, and modeling, have been conducted to identify ways to mitigate these uncertainties. However, various protocols have been developed in different WBE programs for wastewater sampling and analysis, making cross-comparisons of data obtained by different protocols challenging. Establishing a standard or best-practice WBE protocol for sampling, analysis, and reporting would be beneficial. Additionally, we will demonstrate how machine learning can enable AI-powered WBE to become a reliable surveillance tool.
Guangming Jiang (Tue,) studied this question.
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