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Khurana MP, Curran-Sebastian J, Scheidwasser N, Morgenstern C, Rasmussen M, Fonager J, Stegger M, Tang MHE, Juul JL, Escobar-Herrera LA, Møller FT, Albertsen M, Kraemer MUG, du Plessis L, Jokelainen P, Lehmann S, Krause TG, Ullum H, Duchêne DA, Mortensen LH, Bhatt S. High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark. Nat Commun 2024; 15:7123. [PMID: 39164246 PMCID: PMC11335946 DOI: 10.1038/s41467-024-51371-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 08/01/2024] [Indexed: 08/22/2024] Open
Abstract
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.
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Affiliation(s)
- Mark P Khurana
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
| | - Jacob Curran-Sebastian
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Neil Scheidwasser
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Christian Morgenstern
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Morten Rasmussen
- Virus Research and Development Laboratory, Statens Serum Institut, Copenhagen, Denmark
| | - Jannik Fonager
- Virus Research and Development Laboratory, Statens Serum Institut, Copenhagen, Denmark
| | - Marc Stegger
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
- Antimicrobial Resistance and Infectious Diseases Laboratory, Harry Butler Institute, Murdoch University, Murdoch, WA, Australia
| | - Man-Hung Eric Tang
- Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
| | - Jonas L Juul
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | | | - Mads Albertsen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Pikka Jokelainen
- Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark
| | - Sune Lehmann
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Tyra G Krause
- Epidemiological Infectious Disease Preparedness, Statens Serum Institut Copenhagen, Copenhagen, Denmark
| | | | - David A Duchêne
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Laust H Mortensen
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Statistics Denmark, Copenhagen, Denmark
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
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McCabe R, Danelian G, Panovska-Griffiths J, Donnelly CA. Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey. Infect Dis Model 2024; 9:299-313. [PMID: 38371874 PMCID: PMC10867655 DOI: 10.1016/j.idm.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/20/2024] Open
Abstract
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.
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Affiliation(s)
- Ruth McCabe
- Department of Statistics, University of Oxford, UK
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, UK
- United Kingdom Health Security Agency, UK
| | | | - Jasmina Panovska-Griffiths
- United Kingdom Health Security Agency, UK
- The Queen's College, University of Oxford, UK
- The Pandemic Sciences Institute, University of Oxford, UK
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, UK
- National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, UK
- The Pandemic Sciences Institute, University of Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, UK
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Cahuantzi R, Lythgoe KA, Hall I, Pellis L, House T. Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proc Natl Acad Sci U S A 2024; 121:e2317284121. [PMID: 38478692 PMCID: PMC10962941 DOI: 10.1073/pnas.2317284121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024] Open
Abstract
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.
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Affiliation(s)
- Roberto Cahuantzi
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
- United Kingdom Health Security Agency, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Katrina A. Lythgoe
- Department of Biology, University of Oxford, OxfordOX1 3SZ, United Kingdom
- Big Data Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Ian Hall
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Thomas House
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
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Hilti D, Wehrli F, Berchtold S, Bigler S, Bodmer T, Seth-Smith HMB, Roloff T, Kohler P, Kahlert CR, Kaiser L, Egli A, Risch L, Risch M, Wohlwend N. 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. Microorganisms 2024; 12:321. [PMID: 38399725 PMCID: PMC10892681 DOI: 10.3390/microorganisms12020321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/24/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
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.
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Affiliation(s)
- Dominique Hilti
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
- Institute of Laboratory Medicine, Private University in the Principality of Liechtenstein (UFL), 9495 Triesen, Liechtenstein
| | - Faina Wehrli
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
| | | | - Susanna Bigler
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
| | - Thomas Bodmer
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
| | | | - Tim Roloff
- Institute of Medical Microbiology, University of Zurich, 8006 Zurich, Switzerland
| | - Philipp Kohler
- Zentrallabor, Kantonsspital Graubünden, 7000 Chur, Switzerland
| | - Christian R. Kahlert
- Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, 9007 St. Gallen, Switzerland
| | - Laurent Kaiser
- Division of Infectious Diseases, Geneva University Hospitals, 1205 Geneva, Switzerland
- Geneva Centre for Emerging Viral Diseases, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - Adrian Egli
- Institute of Medical Microbiology, University of Zurich, 8006 Zurich, Switzerland
| | - Lorenz Risch
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
- Institute of Laboratory Medicine, Private University in the Principality of Liechtenstein (UFL), 9495 Triesen, Liechtenstein
| | - Martin Risch
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
- Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, 9007 St. Gallen, Switzerland
| | - Nadia Wohlwend
- Laboratory Dr. Risch, 9470 Buchs, Switzerland (L.R.); (N.W.)
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Ealand CS, Gordhan BG, Machowski EE, Kana BD. Development of primer-probe sets to rapidly distinguish single nucleotide polymorphisms in SARS-CoV-2 lineages. Front Cell Infect Microbiol 2023; 13:1283328. [PMID: 38130775 PMCID: PMC10733533 DOI: 10.3389/fcimb.2023.1283328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/10/2023] [Indexed: 12/23/2023] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Bavesh D. Kana
- Department of Science and Innovation/National Research Foundation Centre of Excellence for Biomedical Tuberculosis (TB) Research, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand and The National Health Laboratory Service, Johannesburg, South Africa
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