QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

Sapoval, Nicolae; Lou, Esther; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Treangen, Todd J.; Stadler, Lauren B.

doi:10.1101/2021.09.08.21263279

Cited by 9 publications

(15 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A potential explanation could be that cases are only reported once when an infection is detected whereas virus material can be found in wastewater for a longer period. This seems to be dependent on the respective virus variant as indicated earlier in the literature, where shedding patterns were found to differ between virus variants (Bertels et al, 2022; Sapoval et al, 2021; Wurtzer et al, 2022). It may be worthwhile to train a separate model (and lag) for each slope of the data to enhance coherence.…”

Section: Discussionsupporting

confidence: 67%

Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

Lehmann

Geißler

Hahn

et al. 2022

Preprint

View full text Add to dashboard Cite

In the ongoing SARS-CoV-2 pandemic, there is a need for new strategies for surveillance and identification of arising infection waves. Reported cases of new infections based on individual testing are soon deemed inaccurate due to ever changing regulations and limited testing capacity. Wastewater based epidemiology is one promising solution that can be broadly applied with low efforts in comparison to current large-scale testing of individuals. Here, we are combining local wastewater data from the city of Dresden (Germany) along with reported cases and vaccination data from a central database (Robert-Koch-Institute) with virus variant information to investigate the correlation of virus concentrations in the wastewater and reported SARS-CoV-2 cases. In particular, we compared Linear Regression and Machine Learning (ML) models, which are both revealing an existing correlation of virus particles in wastewater and reported cases. Our findings demonstrate that the different virus variants of concern (Alpha, Delta, BA.1, and BA.2) contribute differently over time and parameters vary between variants, as well. By comparing the Linear Regression and ML-based models, we observed that ML can achieve a good fit for training data, but Linear Regression is a more robust tool, especially for new virus variants. We hereby conclude that deriving the rate of new infections from local wastewater by applying Linear Regression may be a robust approximation of tracing the state of the pandemic for practitioners and policy makers alike.

show abstract

Section: Discussionsupporting

confidence: 67%

Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

Lehmann

Geißler

Hahn

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Methods for the quantification of previously described and/or de novo variants from the complex genotype mixtures in WW are being developed but not well established yet 42,[67][68][69] . Our approach to assign mutations to mutation constellations based on their individual mutation frequencies across several samples using DeViVa serves as a proof of principle and can be integrated directly with the variant definition-guided VaQuERo approach to identify emerging mutation constellations and investigate their temporal and spatial development patterns.…”

Section: Discussionmentioning

confidence: 99%

Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale

et al. 2022

View full text Add to dashboard Cite

SARS-CoV-2 surveillance by wastewater-based epidemiology is poised to provide a complementary approach to sequencing individual cases. However, robust quantification of variants and de novo detection of emerging variants remains challenging for existing strategies. We deep sequenced 3,413 wastewater samples representing 94 municipal catchments, covering >59% of the population of Austria, from December 2020 to February 2022. Our system of variant quantification in sewage pipeline designed for robustness (termed VaQuERo) enabled us to deduce the spatiotemporal abundance of predefined variants from complex wastewater samples. These results were validated against epidemiological records of >311,000 individual cases. Furthermore, we describe elevated viral genetic diversity during the Delta variant period, provide a framework to predict emerging variants and measure the reproductive advantage of variants of concern by calculating variant-specific reproduction numbers from wastewater. Together, this study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without extensive individual monitoring.

show abstract

“…Tracing signature mutations, or quasi-signature mutations [44], does not allow in general for the reconstructing of full-length haplotypes of SARS-CoV-2 and thus assessing the combinations of characteristic mutations. We show that for genomic regions enriched in mutations, haplotype reconstruction based on SNP co-occurrence could be feasible, but we were mainly able to predict short haplotypes for abundant lineages.…”

Section: Discussionmentioning

confidence: 99%

Genome Sequencing of SARS-CoV-2 Allows Monitoring of Variants of Concern through Wastewater

Herold

d’Hérouël

Delogu

et al. 2021

Water

View full text Add to dashboard Cite

Monitoring SARS-CoV-2 in wastewater has shown to be an effective tool for epidemiological surveillance. More specifically, RNA levels determined with RT-qPCR have been shown to track with the infection dynamics within the population. However, the surveillance of individual lineages circulating in the population based on genomic sequencing of wastewater samples is challenging, as the genetic material constitutes a mixture of different viral haplotypes. Here, we identify specific signature mutations from individual SARS-CoV-2 lineages in wastewater samples to estimate lineages circulating in Luxembourg. We compare circulating lineages and mutations to those detected in clinical samples amongst infected individuals. We show that especially for dominant lineages, the allele frequencies of signature mutations correspond to the occurrence of particular lineages in the population. In addition, we provide evidence that regional clusters can also be discerned. We focused on the time period between November 2020 and March 2021 in which several variants of concern emerged and specifically traced the lineage B.1.1.7, which became dominant in Luxembourg during that time. During the subsequent time points, we were able to reconstruct short haplotypes, highlighting the co-occurrence of several signature mutations. Our results highlight the potential of genomic surveillance in wastewater samples based on amplicon short-read data. By extension, our work provides the basis for the early detection of novel SARS-CoV-2 variants.

show abstract

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

Cited by 9 publications

References 62 publications

Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

Efficacy of Linear Regression Modelling of SARS-CoV-2 cases based on local wastewater surveillance

Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale

Genome Sequencing of SARS-CoV-2 Allows Monitoring of Variants of Concern through Wastewater

Contact Info

Product

Resources

About