Wastewater-Based Genomic Epidemiology

The COVID-19 pandemic was shaped by the emergence of viral variants with increased transmissibility or immune escape, which drove successive waves of infection and reinfection. Genomic surveillance has become a cornerstone of pathogen monitoring, but clinical sequencing remains expensive, logistically demanding and biased by non-random testing. Wastewater–based epidemiology (WBE), whose origins trace back almost a century, has only recently emerged as a practical tool for large-scale, real-time surveillance of viral diseases at the population level. It promises to offer a cost-effective, unbiased, and privacy-preserving complement to clinical data, but tracking genomic variants in wastewater sequencing poses unique analytical challenges due to the mixed nature of samples, degraded RNA, and high noise levels. This thesis addresses these challenges by developing and validating statistical and computational methods specifically tailored to wastewater genomic surveillance, and by rigorously evaluating their robustness and epidemiological value. First, I demonstrate that wastewater sequencing enables the early detection of newly introduced variants, in some cases outperforming even extensive clinical sequencing campaigns. I then develop and introduce new tools for wastewater data analysis: one for estimating the relative abundance of variants in mixed samples, shown to perform robustly even under severe noise and missing data ; and another for modeling competition between variants, enabling efficient estimation of selection advantages and accurate forecasts of variant dynamics. Both of these approaches are computationally efficient, scalable, and designed for deployment in real-time surveillance systems. Finally, I examine the influence of viral shedding profiles on the inference of key epidemiological parameters, demonstrating that for estimates of selection and reproduction rates, wastewater-based surveillance remains unbiased and robust across a wide range of plausible scenarios. Together, this work establishes wastewater sequencing as an effective and practical framework for viral genomic epidemiology, and provides methods that are now integrated into routine national surveillance efforts.

Grantor dc:publisher

ETH Zurich

Year dc:date

2025

Author dc:creator

• Dreifuss , David

Contributors dc:contributor

• Beerenwinkel, Niko; id_orcid0000-0002-0573-6119
• Julian , Timothy
• Stadler, Tanja; id_orcid0000-0002-0573-6119
• Bühlmann , Peter

• Natural sciences

Statement dc:rights

• info:eu-repo/semantics/openAccess
• Creative Commons Attribution 4.0 International

Licence

• http://creativecommons.org/licenses/by/4.0/

Language dc:language

en

Identifier

https://doi.org/10.3929/ethz-c-000790390

OAI identifier oai:identifier

oai:www.research-collection.ethz.ch:20.500.11850/790390