Wastewater genomic surveillance tracks the spread of the SARS-CoV-2 Omicron variant across England (preprint)

Background Many countries have moved into a new stage of managing the SARS-CoV-2 pandemic with minimal restrictions and reduced testing in the population, leading to reduced genomic surveillance of virus variants in individuals. Wastewater-based epidemiology (WBE) can provide an alternative means of tracking virus variants in the population but is lacking verifications of its comparability to individual testing data. Methods We analysed more than 19,000 samples from 524 wastewater sites across England at least twice a week between November 2021 and February 2022, capturing sewage from >70% of the English population. We used amplicon-based sequencing and the phylogeny based de-mixing tool Freyja to estimate SARS-CoV-2 variant frequencies and compared these to the variant dynamics observed in individual testing data from clinical and community settings. Findings We show that wastewater data can reconstruct the spread of the Omicron variant across England since November 2021 in close detail and aligns closely with epidemiological estimates from individual testing data. We also show the temporal and spatial spread of Omicron within London. Our wastewater data further reliably track the transition between Omicron subvariants BA1 and BA2 in February 2022 at regional and national levels. Interpretation Our demonstration that WBE can track the fast-paced dynamics of SARS-CoV-2 variant frequencies at a national scale and closely match individual testing data in time shows that WBE can reliably fill the monitoring gap left by reduced individual testing in a more affordable way.

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic (preprint)

The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks. We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2 N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus-associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation. We evaluate the models predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks). The proposed statistical framework is able to predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic quantification of local changes can be used as an early warning tool for public health surveillance.

Comparison of variant callers for wastewater-based epidemiology (preprint)

Wastewater-based epidemiology (WBE) has been extensively used during the COVID-19 pandemic to detect and monitor the spread of the SARS-CoV-2 virus and its variants. It has also proven to be an excellent tool to complement and support insights gained from reported clinical data. Globally, many groups have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations from RNA extracted from wastewater samples is key in supporting clinical data to make informed decisions on the prevalence of variants, as well as in the use of WBE as a molecular surveillance tool. However, wastewater samples can be challenging to extract and sequence, and performance of variant-calling algorithms in this context has, so far, not been investigated. Analysis of the data and assignment of circulating variants depends heavily on the accuracy of the variant caller, particularly given the degraded nature of the viral RNA and the heterogeneous nature of metagenomic samples. To address this, we compared the performance of six variant callers (VarScan, iVAR, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with a known mix of three different SARS-CoV-2 variant genomes (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15 th and 18 th December 2021. Using the Quasimodo benchmarking tool to compare the six variant callers, we assessed the fundamental parameters of recall (sensitivity) and precision (specificity) in confirming the presence of a variant within the population. Our results show that BCFtools, FreeBayes and VarScan called the expected mutations with higher precision and recall than iVAR or GATK, although the latter identified more expected defining mutations. LoFreq gave the least reliable results due to the high number of false positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.