Abstract
Wastewater-based epidemiology (WBE) provides a cost-effective and non-invasive approach to monitoring infectious diseases, antimicrobial resistance, and chemical exposures at the community level. It has rapidly evolved into a critical tool for tracking public health, particularly during the COVID-19 pandemic. However, its application is constrained by the inherent complexity of sewage systems.

This talk examines key sources of uncertainty in applying WBE to COVID-19, such as spatiotemporal variability, environmental confounders, and analytical limitations. It also demonstrates how artificial intelligence (AI) can address these challenges by integrating complex datasets, uncovering hidden patterns, and improving predictive capability. Drawing on recent studies, I will show how AI-driven approaches enhance outbreak prediction, assess regional vulnerability, and support timely public health responses during health crises. By embracing complexity through AI, WBE can become a more reliable, scalable, and proactive surveillance framework for safeguarding global health.

Bio

Dr. Xuan Li is an ARC DECRA Fellow in the School of Civil and Environmental Engineering at the University of Technology Sydney (UTS). She received her PhD from the University of Queensland in 2020. Her research focuses on wastewater-based epidemiology, concrete corrosion control, and wastewater treatment.

Dr. Li has led numerous research and industry projects and has published more than 90 high-impact papers. She also serves as a handling editor and editorial board member for several leading Q1 journals, including Chemical Engineering Journal (Elsevier). In recognition of her achievements, she was recently awarded the FEIT Early or Mid-Career Researcher of the Year at UTS.