Estimated transmission dynamics of SARS-CoV-2 variants from wastewater are robust to differential shedding (preprint)

The COVID-19 pandemic has accelerated the development and adoption of wastewater-based epidemiology. Wastewater samples can provide genomic information for detecting and assessing the spread of SARS-CoV-2 variants in communities and for estimating important epidemiological parameters such as the growth advantage of the variant. However, despite demonstrated successes, epidemiological data derived from wastewater suffers from potential biases. Of particular concern are differential shedding profiles that different variants of concern exhibit, because they can shift the relationship between viral loads in wastewater and prevalence estimates derived from clinical cases. Using mathematical modeling, simulations, and Swiss surveillance data, we demonstrate that this bias does not affect estimation of the growth advantage of the variant and has only a limited and transient impact on estimates of the effective reproduction number. Thus, population-level epidemiological parameters derived from wastewater maintain their advantages over traditional clinical-derived estimates, even in the presence of differential shedding among variants.

Inferring Transmission Fitness Advantage of SARS-CoV-2 Variants of Concern in Wastewater Using Digital PCR (preprint)

Throughout the global COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterized by increased transmissibility, increased virulence, or reduced neutralization by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole-genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches. Here, we adapt and apply a rapid, high-throughput method for detection and quantification of the frequency of two deletions characteristic of the B.1.1.7, B.1.351, and P.1 VOCs in wastewater. We further develop a statistical approach to analyze temporal dynamics in drop-off RT-dPCR assay data to quantify transmission fitness advantage, providing data similar to that obtained from clinical samples. Digital PCR assays targeting signature mutations in wastewater offer near real-time monitoring of SARS-CoV-2 VOCs and potentially earlier detection and inference on transmission fitness advantage than clinical sequencing.