Lineage replacement and evolution captured by 3 years of the United Kingdom Coronavirus (COVID-19) Infection Survey

High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark

Vast amounts of pathogen genomic, demographic and spatial data are transforming our understanding of SARS-CoV-2 emergence and spread. We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged  < 15 and  > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey

Key epidemiological parameters, including the effective reproduction number, R ( t ) , and the instantaneous growth rate, r ( t ) , generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the "emergency" to "endemic" phase of the pandemic. The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the "ONS-based" R ( t ) and r ( t ) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters. Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates. Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants.

S-Gene Target Failure as an Effective Tool for Tracking the Emergence of Dominant SARS-CoV-2 Variants in Switzerland and Liechtenstein, Including Alpha, Delta, and Omicron BA.1, BA.2, and BA.4/BA.5

During the SARS-CoV-2 pandemic, the Dr. Risch medical group employed the multiplex TaqPathTM COVID-19 CE-IVD RT-PCR Kit for large-scale routine diagnostic testing in Switzerland and the principality of Liechtenstein. The TaqPath Kit is a widely used multiplex assay targeting three genes (i.e., ORF1AB, N, S). With emergence of the B.1.1.7 (Alpha) variant, a diagnostic flaw became apparent as the amplification of the S-gene target was absent in these samples due to a deletion (ΔH69/V70) in the Alpha variant genome. This S-gene target failure (SGTF) was the earliest indication of a new variant emerging and was also observed in subsequent variants such as Omicron BA.1 and BA4/BA.5. The Delta variant and Omicron BA.2 did not present with SGTF. From September 2020 to November 2022, we investigated the applicability of the SGTF as a surrogate marker for emerging variants such as B.1.1.7, B.1.617.2 (Delta), and Omicron BA.1, BA.2, and BA.4/BA.5 in samples with cycle threshold (Ct) values < 30. Next to true SGTF-positive and SGTF-negative samples, there were also samples presenting with delayed-type S-gene amplification (higher Ct value for S-gene than ORF1ab gene). Among these, a difference of 3.8 Ct values between the S- and ORF1ab genes was found to best distinguish between "true" SGTF and the cycle threshold variability of the assay. Samples above the cutoff were subsequently termed partial SGTF (pSGTF). Variant confirmation was performed by whole-genome sequencing (Oxford Nanopore Technology, Oxford, UK) or mutation-specific PCR (TIB MOLBIOL). In total, 17,724 (7.4%) samples among 240,896 positives were variant-confirmed, resulting in an overall sensitivity and specificity of 93.2% [92.7%, 93.7%] and 99.3% [99.2%, 99.5%], respectively. Sensitivity was increased to 98.2% [97.9% to 98.4%] and specificity lowered to 98.9% [98.6% to 99.1%] when samples with pSGTF were included. Furthermore, weekly logistic growth rates (α) and sigmoid's midpoint (t0) were calculated based on SGTF data and did not significantly differ from calculations based on comprehensive data from GISAID. The SGTF therefore allowed for a valid real-time estimate for the introduction of all dominant variants in Switzerland and Liechtenstein.

Development of primer-probe sets to rapidly distinguish single nucleotide polymorphisms in SARS-CoV-2 lineages

Ongoing SARS-CoV-2 infections are driven by the emergence of various variants, with differential propensities to escape immune containment. Single nucleotide polymorphisms (SNPs) in the RNA genome result in altered protein structures and when these changes occur in the S-gene, encoding the spike protein, the ability of the virus to penetrate host cells to initiate an infection can be significantly altered. As a result, vaccine efficacy and prior immunity may be diminished, potentially leading to new waves of infection. Early detection of SARS-CoV-2 variants using a rapid and scalable approach will be paramount for continued monitoring of new infections. In this study, we developed minor groove-binding (MGB) probe-based qPCR assays targeted to specific SNPs in the S-gene, which are present in variants of concern (VOC), namely the E484K, N501Y, G446S and D405N mutations. A total of 95 archived SARS-CoV-2 positive clinical specimens collected in Johannesburg, South Africa between February 2021 and March 2022 were assessed using these qPCR assays. To independently confirm SNP detection, Sanger sequencing of the relevant region in the S-gene were performed. Where a PCR product could be generated and sequenced, qPCR assays were 100% concordant highlighting the robustness of the approach. These assays, and the approach described, offer the opportunity for easy detection and scaling of targeted detection of variant-defining SNPs in the clinical setting.