A wastewater approach to viral transmission, evolution, and ecology

Monitoring viral dynamics at the community level is crucial for effective public health responses, yet remains a significant challenge. Understanding how viruses transmit, evolve, and interact ecologically within populations is essential for predicting and managing outbreaks. Traditional viral surveillance methods, which rely primarily on clinical testing and analysis of samples from symptomatic individuals, have been invaluable but have recognized constraints. These approaches often miss asymptomatic cases, are slow to detect new variants, and fail to capture the full spectrum of viral behavior within communities. Consequently, there is a pressing need for more comprehensive monitoring strategies that can provide a more complete and timely understanding of viral dynamics at the population level. Wastewater-based surveillance has emerged as a promising complement to traditional surveillance methods, offering a unique window into community-wide viral activity. By analyzing sewage, researchers can detect viral genetic material shed by both symptomatic and asymptomatic individuals, providing a more inclusive snapshot of infection patterns. However, current wastewater surveillance research faces two main challenges: first, translating wastewater data into precise epidemiological insights for quantitative understanding and prediction of viral transmission; and second, leveraging this approach to understand variant evolution and ecology within communities. As viruses mutate and generate diverse variant populations, how different variants compete, coexist, or give rise to new strains at the community level remains largely unexplored. To address these challenges, the long-term goal of this project is to establish a cohesive framework for understanding and predicting viral dynamics at the community level. By integrating wastewater surveillance, computational modeling, and genomic sequencing, we will pursue three specific aims: 1) Develop advanced models combining within-host viral kinetics and between-host transmission dynamics to improve wastewater data interpretation and prediction of viral transmission; 2) Examine the evolutionary dynamics of variant transitions, focusing on the Delta-to-Omicron shift as a case study; and 3) Analyze variant community dynamics and ecological interactions across multiple cities to understand variant succession patterns and biodiversity, and predict the emergence of new dominant variants. This integrated approach will significantly advance our understanding of viral dynamics at the community level, enhancing our ability to detect new viral threats early, track their evolution, and understand their ecological interactions. Through strategic collaboration with the Wastewater Action Plan committee of the Texas Epidemic Public Health Institute, this work will influence public health policy and decision-making, strengthen pandemic preparedness, and improve our capacity to respond effectively to future viral outbreaks. Project Number: 1R01AI192873-01 | Fiscal Year: 2025 | NIH Institute/Center: National Institute of Allergy and Infectious Diseases (NIAID) | Principal Investigator: Fuqing Wu | Institution: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON, HOUSTON, TX | Award Amount: $568,909 | Activity Code: R01 | Study Section: Special Emphasis Panel[ZRG1 DCAI-S (81)] View on NIH RePORTER: https://reporter.nih.gov/project-details/1R01AI19287301