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During the COVID-19 pandemic, wastewater-based epidemiology has progressively taken a central role as a pathogen surveillance tool. Tracking viral loads and variant outbreaks in sewage offers advantages over clinical surveillance methods by providing unbiased estimates and enabling early detection. However, wastewater-based epidemiology poses new computational research questions that need to be solved in order for this approach to be implemented broadly and successfully. Here, we address the variant deconvolution problem, where we aim to estimate the relative abundances of genomic variants from next-generation sequencing data of a mixed wastewater sample. We introduce LolliPop, a computational method to solve the variant deconvolution problem by simultaneously solving least squares problems and kernel-based smoothing of relative variant abundances from wastewater time series sequencing data. We derive multiple approaches to compute confidence bands, and demonstrate the application of our method to data from the Swiss wastewater surveillance efforts.
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Genome sequences from evolving infectious pathogens allow quantification of case introductions and local transmission dynamics. We sequenced 11,357 SARS-CoV-2 genomes from Switzerland in 2020 - the 6th largest effort globally. Using a representative subset of these data, we estimated viral introductions to Switzerland and their persistence over the course of 2020. We contrast these estimates with simple null models representing the absence of certain public health measures. We show that Switzerlands border closures de-coupled case introductions from incidence in neighboring countries. Under a simple model, we estimate an 86 - 98% reduction in introductions during Switzerlands strictest border closures. Furthermore, the Swiss 2020 partial lockdown roughly halved the time for sampled introductions to die out. Finally, we quantified local transmission dynamics once introductions into Switzerland occurred, using a novel phylodynamic model. We find that transmission slowed 35 - 63% upon outbreak detection in summer 2020, but not in fall. This finding may indicate successful contact tracing over summer before overburdening in fall. The study highlights the added value of genome sequencing data for understanding transmission dynamics. One Sentence SummaryPhylogenetic and phylodynamic methods quantify the drop in case introductions and local transmission with implementation of public health measures.
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BackgroundIn December 2020, the United Kingdom (UK) reported a SARS-CoV-2 Variant of Concern (VoC) which is now named B.1.1.7. Based on initial data from the UK and later data from other countries, this variant was estimated to have a transmission fitness advantage of around 40-80% [1, 2, 3]. AimThis study aims to estimate the transmission fitness advantage and the effective reproductive number of B.1.1.7 through time based on data from Switzerland. MethodsWe generated whole genome sequences from 11.8% of all confirmed SARS-CoV-2 cases in Switzerland between 14 December 2020 and 11 March 2021. Based on these data, we determine the daily frequency of the B.1.1.7 variant and quantify the variants transmission fitness advantage on a national and a regional scale. ResultsWe estimate B.1.1.7 had a transmission fitness advantage of 43-52% compared to the other variants circulating in Switzerland during the study period. Further, we estimate B.1.1.7 had a reproductive number above 1 from 01 January 2021 until the end of the study period, compared to below 1 for the other variants. Specifically, we estimate the reproductive number for B.1.1.7 was 1.24 [1.07-1.41] from 01 January until 17 January 2021 and 1.18 [1.06-1.30] from 18 January until 01 March 2021 based on the whole genome sequencing data. From 10 March to 16 March 2021, once B.1.1.7 was dominant, we estimate the reproductive number was 1.14 [1.00-1.26] based on all confirmed cases. For reference, Switzerland applied more non-pharmaceutical interventions to combat SARS-CoV-2 on 18 January 2021 and lifted some measures again on 01 March 2021. ConclusionThe observed increase in B.1.1.7 frequency in Switzerland during the study period is as expected based on observations in the UK. In absolute numbers, B.1.1.7 increased exponentially with an estimated doubling time of around 2-3.5 weeks. To monitor the ongoing spread of B.1.1.7, our plots are available online.
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The emergence of SARS-CoV-2 mutants with altered transmissibility, virulence, or immunogenicity emphasizes the need for early detection and epidemiological surveillance of genomic variants. Wastewater samples provide an opportunity to assess circulating viral lineages in the community. We performed genomic sequencing of 122 wastewater samples from three locations in Switzerland to analyze the B.1.1.7, B.1.351, and P.1 variants of SARS-CoV-2 on a population level. We called variant-specific signature mutations and monitored variant prevalence in the local population over time. To enable early detection of emerging variants, we developed a bioinformatics tool that uses read pairs carrying multiple signature mutations as a robust indicator of low-frequency variants. We further devised a statistical approach to estimate the transmission fitness advantage, a key epidemiological parameter indicating the speed at which a variant spreads through the population, and compared the wastewater-based findings to those derived from clinical samples. We found that the local outbreak of the B.1.1.7 variant in two Swiss cities was observable in wastewater up to 8 days before its first detection in clinical samples. We detected a high prevalence of the B.1.1.7 variant in an alpine ski resort popular among British tourists in December 2020, a time when the variant was still very rare in Switzerland. We found no evidence of local spread of the B.1.351 and P.1 variants at the monitored locations until the end of the study (mid February) which is consistent with clinical samples. Estimation of local variant prevalence performs equally well or better for wastewater samples as for a much larger number of clinical samples. We found that the transmission fitness advantage of B.1.1.7, i.e. the relative change of its reproductive number, can be estimated earlier and based on substantially fewer wastewater samples as compared to using clinical samples. Our results show that genomic sequencing of wastewater samples can detect, monitor, and evaluate genetic variants of SARS-CoV-2 on a population level. Our methodology provides a blueprint for rapid, unbiased, and cost-efficient genomic surveillance of SARS-CoV-2 variants.
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Pathogen genomes provide insights into their evolution and epidemic spread. We sequenced 1,439 SARS-CoV-2 genomes from Switzerland, representing 3-7% of all confirmed cases per week. Using these data, we demonstrate that no one lineage became dominant, pointing against evolution towards general lower virulence. On an epidemiological level, we report no evidence of cryptic transmission before the first confirmed case. We find many early viral introductions from Germany, France, and Italy and many recent introductions from Germany and France. Over the summer, we quantify the number of non-traceable infections stemming from introductions, quantify the effective reproductive number, and estimate the degree of undersampling. Our framework can be applied to quantify evolution and epidemiology in other locations or for other pathogens based on genomic data. One Sentence SummaryWe quantify SARS-CoV-2 spread in Switzerland based on genome sequences from our nation-wide sequencing effort.
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SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.
