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Suljič A, Zorec TM, Zakotnik S, Vlaj D, Kogoj R, Knap N, Petrovec M, Poljak M, Avšič-Županc T, Korva M. Efficient SARS-CoV-2 variant detection and monitoring with Spike Screen next-generation sequencing. Brief Bioinform 2024; 25:bbae263. [PMID: 38833323 PMCID: PMC11149657 DOI: 10.1093/bib/bbae263] [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: 11/07/2023] [Revised: 04/23/2024] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
The emergence and rapid spread of SARS-CoV-2 prompted the global community to identify innovative approaches to diagnose infection and sequence the viral genome because at several points in the pandemic positive case numbers exceeded the laboratory capacity to characterize sufficient samples to adequately respond to the spread of emerging variants. From week 10, 2020, to week 13, 2023, Slovenian routine complete genome sequencing (CGS) surveillance network yielded 41 537 complete genomes and revealed a typical molecular epidemiology with early lineages gradually being replaced by Alpha, Delta, and finally Omicron. We developed a targeted next-generation sequencing based variant surveillance strategy dubbed Spike Screen through sample pooling and selective SARS-CoV-2 spike gene amplification in conjunction with CGS of individual cases to increase throughput and cost-effectiveness. Spike Screen identifies variant of concern (VOC) and variant of interest (VOI) signature mutations, analyses their frequencies in sample pools, and calculates the number of VOCs/VOIs at the population level. The strategy was successfully applied for detection of specific VOC/VOI mutations prior to their confirmation by CGS. Spike Screen complemented CGS efforts with an additional 22 897 samples sequenced in two time periods: between week 42, 2020, and week 24, 2021, and between week 37, 2021, and week 2, 2022. The results showed that Spike Screen can be applied to monitor VOC/VOI mutations among large volumes of samples in settings with limited sequencing capacity through reliable and rapid detection of novel variants at the population level and can serve as a basis for public health policy planning.
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Affiliation(s)
- Alen Suljič
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Tomaž Mark Zorec
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Samo Zakotnik
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Doroteja Vlaj
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Rok Kogoj
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Nataša Knap
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Miroslav Petrovec
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Mario Poljak
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Tatjana Avšič-Županc
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
| | - Miša Korva
- Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Zaloška cesta 4, 1000 Ljubljana, Slovenia
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Wegner F, Cabrera-Gil B, Tanguy A, Beckmann C, Beerenwinkel N, Bertelli C, Carrara M, Cerutti L, Chen C, Cordey S, Dumoulin A, du Plessis L, Friedli M, Gerth Y, Greub G, Härri A, Hirsch H, Howald C, Huber M, Imhof A, Kaiser L, Kufner V, Leib SL, Leuzinger K, Lleshi E, Martinetti G, Mäusezahl M, Moraz M, Neher R, Nolte O, Ramette A, Redondo M, Risch L, Rohner L, Roloff T, Schläepfer P, Schneider K, Singer F, Spina V, Stadler T, Studer E, Topolsky I, Trkola A, Walther D, Wohlwend N, Zehnder C, Neves A, Egli A. How much should we sequence? An analysis of the Swiss SARS-CoV-2 surveillance effort. Microbiol Spectr 2024; 12:e0362823. [PMID: 38497714 PMCID: PMC11064629 DOI: 10.1128/spectrum.03628-23] [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: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
During the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent-something that is unknown at the time of sequencing-and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses.
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Affiliation(s)
- Fanny Wegner
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
| | - Blanca Cabrera-Gil
- Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | | | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Claire Bertelli
- Clinical Microbiology, University Hospital, Lausanne, Switzerland
| | - Matteo Carrara
- NEXUS Personalized Health Technologies, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | - Chaoran Chen
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Samuel Cordey
- Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
| | | | - Louis du Plessis
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | - Yannick Gerth
- Humanmedizinische Mikrobiologie, Zentrum für Labormedizin, St. Gallen, Switzerland
| | - Gilbert Greub
- Clinical Microbiology, University Hospital, Lausanne, Switzerland
| | | | - Hans Hirsch
- Clinical Virology, University Hospital, Basel, Switzerland
| | | | - Michael Huber
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | | | - Laurent Kaiser
- Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
| | - Verena Kufner
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Stephen L. Leib
- Institute for Infectious Diseases (IFIK), University of Bern, Bern, Switzerland
| | | | - Etleva Lleshi
- Microbiology Department, Synlab, Bioggio, Switzerland
| | - Gladys Martinetti
- Department of Laboratory Medicine, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | | | - Milo Moraz
- Valais Hospital, Central Institute, Sion, Switzerland
| | - Richard Neher
- Biozentrum, University of Basel, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Oliver Nolte
- Humanmedizinische Mikrobiologie, Zentrum für Labormedizin, St. Gallen, Switzerland
| | - Alban Ramette
- Institute for Infectious Diseases (IFIK), University of Bern, Bern, Switzerland
| | | | | | | | - Tim Roloff
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
| | | | | | - Franziska Singer
- NEXUS Personalized Health Technologies, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Valeria Spina
- Department of Laboratory Medicine, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Erik Studer
- Federal Office of Public Health, Bern, Switzerland
| | - Ivan Topolsky
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Alexandra Trkola
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Daniel Walther
- Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | | | - Aitana Neves
- Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | - Adrian Egli
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
| | - the SPSP consortium
- Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
- Clinical Bioinformatics, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
- Genesupport, Geneva, Switzerland
- Viollier AG, Allschwil, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- Clinical Microbiology, University Hospital, Lausanne, Switzerland
- NEXUS Personalized Health Technologies, ETH Zurich, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- Health2030 Genome Center, Geneva, Switzerland
- Laboratory of Virology, Geneva University Hospitals, Geneva, Switzerland
- Valais Hospital, Central Institute, Sion, Switzerland
- Humanmedizinische Mikrobiologie, Zentrum für Labormedizin, St. Gallen, Switzerland
- Biolytix, Witterswil, Switzerland
- Clinical Virology, University Hospital, Basel, Switzerland
- Institute of Medical Virology, University of Zurich, Zurich, Switzerland
- Spitalregion Oberaargau, Langenthal, Switzerland
- Institute for Infectious Diseases (IFIK), University of Bern, Bern, Switzerland
- Microbiology Department, Synlab, Bioggio, Switzerland
- Department of Laboratory Medicine, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Federal Office of Public Health, Bern, Switzerland
- Biozentrum, University of Basel, Switzerland & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- Labor Dr. Risch, Buchs, Switzerland
- Clinical Microbiology, University Hospital, Basel, Switzerland
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3
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Chen Z, Lemey P, Yu H. Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data. THE LANCET. MICROBE 2024; 5:e81-e92. [PMID: 38042165 DOI: 10.1016/s2666-5247(23)00296-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 12/04/2023]
Abstract
Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.
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Affiliation(s)
- Zhiyuan Chen
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory of Clinical and Evolutionary Virology, KU Leuven, Leuven, Belgium
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
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4
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Volz E. Fitness, growth and transmissibility of SARS-CoV-2 genetic variants. Nat Rev Genet 2023; 24:724-734. [PMID: 37328556 DOI: 10.1038/s41576-023-00610-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2023] [Indexed: 06/18/2023]
Abstract
The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.
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Affiliation(s)
- Erik Volz
- Department of Infectious Disease Epidemiology, MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
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5
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Castelán-Sánchez HG, Delaye L, Inward RPD, Dellicour S, Gutierrez B, Martinez de la Vina N, Boukadida C, Pybus OG, de Anda Jáuregui G, Guzmán P, Flores-Garrido M, Fontanelli Ó, Hernández Rosales M, Meneses A, Olmedo-Alvarez G, Herrera-Estrella AH, Sánchez-Flores A, Muñoz-Medina JE, Comas-García A, Gómez-Gil B, Zárate S, Taboada B, López S, Arias CF, Kraemer MUG, Lazcano A, Escalera Zamudio M. Comparing the evolutionary dynamics of predominant SARS-CoV-2 virus lineages co-circulating in Mexico. eLife 2023; 12:e82069. [PMID: 37498057 PMCID: PMC10431917 DOI: 10.7554/elife.82069] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 07/23/2023] [Indexed: 07/28/2023] Open
Abstract
Over 200 different SARS-CoV-2 lineages have been observed in Mexico by November 2021. To investigate lineage replacement dynamics, we applied a phylodynamic approach and explored the evolutionary trajectories of five dominant lineages that circulated during the first year of local transmission. For most lineages, peaks in sampling frequencies coincided with different epidemiological waves of infection in Mexico. Lineages B.1.1.222 and B.1.1.519 exhibited similar dynamics, constituting clades that likely originated in Mexico and persisted for >12 months. Lineages B.1.1.7, P.1 and B.1.617.2 also displayed similar dynamics, characterized by multiple introduction events leading to a few successful extended local transmission chains that persisted for several months. For the largest B.1.617.2 clades, we further explored viral lineage movements across Mexico. Many clades were located within the south region of the country, suggesting that this area played a key role in the spread of SARS-CoV-2 in Mexico.
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Affiliation(s)
- Hugo G Castelán-Sánchez
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Programa de Investigadoras e Investigadores por México, Consejo Nacional de Ciencia y TecnologíaMexico CityMexico
| | - Luis Delaye
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Ingeniería Genética, CINVESTAV-Unidad IrapuatoGuanajuatoMexico
| | - Rhys PD Inward
- Department of Biology, University of OxfordOxfordUnited Kingdom
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de BruxellesBruxellesBelgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU LeuvenLeuvenBelgium
| | - Bernardo Gutierrez
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Department of Biology, University of OxfordOxfordUnited Kingdom
| | | | - Celia Boukadida
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades RespiratoriasMexico CityMexico
| | - Oliver G Pybus
- Department of Biology, University of OxfordOxfordUnited Kingdom
- Department of Pathobiology, Royal Veterinary CollegeLondonUnited Kingdom
| | - Guillermo de Anda Jáuregui
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Programa de Investigadoras e Investigadores por México, Consejo Nacional de Ciencia y TecnologíaMexico CityMexico
- Instituto Nacional de Medicina GenómicaMexico CityMexico
| | | | - Marisol Flores-Garrido
- Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de MéxicoMexico CityMexico
- Departamento de Ciencias de la Computación, CINVESTAV-IPNMexico CityMexico
| | - Óscar Fontanelli
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Ingeniería Genética, CINVESTAV-Unidad IrapuatoGuanajuatoMexico
| | - Maribel Hernández Rosales
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Ingeniería Genética, CINVESTAV-Unidad IrapuatoGuanajuatoMexico
| | - Amilcar Meneses
- Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de MéxicoMexico CityMexico
- Departamento de Ciencias de la Computación, CINVESTAV-IPNMexico CityMexico
| | - Gabriela Olmedo-Alvarez
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Ingeniería Genética, CINVESTAV-Unidad IrapuatoGuanajuatoMexico
| | - Alfredo Heriberto Herrera-Estrella
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Laboratorio de expresión génica y desarrollo en hongos, CINVESTAV-Unidad IrapuatoIrapuatoMexico
| | - Alejandro Sánchez-Flores
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Unidad Universitaria de Secuenciación Masiva y Bioinformática, Instituto de Biotecnología, Universidad Nacional Autónoma de MéxicoChamilpaMexico
| | - José Esteban Muñoz-Medina
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Coordinación de Calidad de Insumos y Laboratorios Especializados, Instituto Mexicano del Seguro SocialMexico CityMexico
| | - Andreu Comas-García
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Facultad de Medicina y Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis PotosíSan Luis PotosíMexico
| | - Bruno Gómez-Gil
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Centro de Investigación en Alimentación y Desarrollo-CIAD, Unidad Regional Mazatlán en Acuicultura y Manejo AmbientalSinaloaMexico
| | - Selene Zárate
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Posgrado en Ciencias Genómicas, Universidad Autónoma de la Ciudad de MéxicoMexico CityMexico
| | - Blanca Taboada
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de MéxicoCuernavacaMexico
| | - Susana López
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de MéxicoCuernavacaMexico
| | - Carlos F Arias
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Departamento de Genética del Desarrollo y Fisiología Molecular, Universidad Nacional Autónoma de MéxicoCuernavacaMexico
| | - Moritz UG Kraemer
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Department of Biology, University of OxfordOxfordUnited Kingdom
| | - Antonio Lazcano
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Facultad de Ciencias, Universidad Nacional Autónoma de MéxicMexico CityMexico
| | - Marina Escalera Zamudio
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex)Mexico CityMexico
- Department of Biology, University of OxfordOxfordUnited Kingdom
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6
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Xie R, Edwards KM, Adam DC, Leung KSM, Tsang TK, Gurung S, Xiong W, Wei X, Ng DYM, Liu GYZ, Krishnan P, Chang LDJ, Cheng SMS, Gu H, Siu GKH, Wu JT, Leung GM, Peiris M, Cowling BJ, Poon LLM, Dhanasekaran V. Resurgence of Omicron BA.2 in SARS-CoV-2 infection-naive Hong Kong. Nat Commun 2023; 14:2422. [PMID: 37105966 PMCID: PMC10134727 DOI: 10.1038/s41467-023-38201-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 04/20/2023] [Indexed: 04/29/2023] Open
Abstract
Hong Kong experienced a surge of Omicron BA.2 infections in early 2022, resulting in one of the highest per-capita death rates of COVID-19. The outbreak occurred in a dense population with low immunity towards natural SARS-CoV-2 infection, high vaccine hesitancy in vulnerable populations, comprehensive disease surveillance and the capacity for stringent public health and social measures (PHSMs). By analyzing genome sequences and epidemiological data, we reconstructed the epidemic trajectory of BA.2 wave and found that the initial BA.2 community transmission emerged from cross-infection within hotel quarantine. The rapid implementation of PHSMs suppressed early epidemic growth but the effective reproduction number (Re) increased again during the Spring festival in early February and remained around 1 until early April. Independent estimates of point prevalence and incidence using phylodynamics also showed extensive superspreading at this time, which likely contributed to the rapid expansion of the epidemic. Discordant inferences based on genomic and epidemiological data underscore the need for research to improve near real-time epidemic growth estimates by combining multiple disparate data sources to better inform outbreak response policy.
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Affiliation(s)
- Ruopeng Xie
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kimberly M Edwards
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Dillon C Adam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Kathy S M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tim K Tsang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Shreya Gurung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Weijia Xiong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Xiaoman Wei
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Daisy Y M Ng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gigi Y Z Liu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Pavithra Krishnan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lydia D J Chang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Samuel M S Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gilman K H Siu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Joseph T Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Gabriel M Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Vijaykrishna Dhanasekaran
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- HKU-Pasteur Research Pole, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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Tosta S, Moreno K, Schuab G, Fonseca V, Segovia FMC, Kashima S, Elias MC, Sampaio SC, Ciccozzi M, Alcantara LCJ, Slavov SN, Lourenço J, Cella E, Giovanetti M. Global SARS-CoV-2 genomic surveillance: What we have learned (so far). INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2023; 108:105405. [PMID: 36681102 PMCID: PMC9847326 DOI: 10.1016/j.meegid.2023.105405] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 01/20/2023]
Abstract
The COVID-19 pandemic has brought significant challenges for genomic surveillance strategies in public health systems worldwide. During the past thirty-four months, many countries faced several epidemic waves of SARS-CoV-2 infections, driven mainly by the emergence and spread of novel variants. In that line, genomic surveillance has been a crucial toolkit to study the real-time SARS-CoV-2 evolution, for the assessment and optimization of novel diagnostic assays, and to improve the efficacy of existing vaccines. During the pandemic, the identification of emerging lineages carrying lineage-specific mutations (particularly those in the Receptor Binding domain) showed how these mutations might significantly impact viral transmissibility, protection from reinfection and vaccination. So far, an unprecedented number of SARS-CoV-2 viral genomes has been released in public databases (i.e., GISAID, and NCBI), achieving 14 million genome sequences available as of early-November 2022. In the present review, we summarise the global landscape of SARS-CoV-2 during the first thirty-four months of viral circulation and evolution. It demonstrates the urgency and importance of sustained investment in genomic surveillance strategies to timely identify the emergence of any potential viral pathogen or associated variants, which in turn is key to epidemic and pandemic preparedness.
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Affiliation(s)
- Stephane Tosta
- Interunit Postgraduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Keldenn Moreno
- Interunit Postgraduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Gabriel Schuab
- Federal University of Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil; Laboratório de Flavivirus, Instituto Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Vagner Fonseca
- Organização Pan-Americana da Saúde/Organização Mundial da Saúde, Brasília, Distrito Federal, Brazil.
| | | | - Simone Kashima
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo,Brazil
| | | | | | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy
| | - Luiz Carlos Junior Alcantara
- Interunit Postgraduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Laboratório de Flavivirus, Instituto Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Svetoslav Nanev Slavov
- Blood Center of Ribeirão Preto, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo,Brazil; Butantan Institute, São Paulo, Brazil
| | - José Lourenço
- BioISI (Biosystems and Integrative Sciences Institute), Faculdade de Ciências da Universidade de Lisboa, Lisboa,Portugal
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL 32827, USA.
| | - Marta Giovanetti
- Interunit Postgraduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Laboratório de Flavivirus, Instituto Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil; Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, Italy.
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Inward RP, Jackson F, Dasgupta A, Lee G, Battle AL, Parag KV, Kraemer MU. Impact of spatiotemporal heterogeneity in COVID-19 disease surveillance on epidemiological parameters and case growth rates. Epidemics 2022; 41:100627. [PMID: 36099708 PMCID: PMC9443927 DOI: 10.1016/j.epidem.2022.100627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/04/2022] [Accepted: 09/03/2022] [Indexed: 02/08/2023] Open
Abstract
SARS-CoV-2 case data are primary sources for estimating epidemiological parameters and for modelling the dynamics of outbreaks. Understanding biases within case-based data sources used in epidemiological analyses is important as they can detract from the value of these rich datasets. This raises questions of how variations in surveillance can affect the estimation of epidemiological parameters such as the case growth rates. We use standardised line list data of COVID-19 from Argentina, Brazil, Mexico and Colombia to estimate delay distributions of symptom-onset-to-confirmation, -hospitalisation and -death as well as hospitalisation-to-death at high spatial resolutions and throughout time. Using these estimates, we model the biases introduced by the delay from symptom-onset-to-confirmation on national and state level case growth rates (rt) using an adaptation of the Richardson-Lucy deconvolution algorithm. We find significant heterogeneities in the estimation of delay distributions through time and space with delay difference of up to 19 days between epochs at the state level. Further, we find that by changing the spatial scale, estimates of case growth rate can vary by up to 0.13 d-1. Lastly, we find that states with a high variance and/or mean delay in symptom-onset-to-diagnosis also have the largest difference between the rt estimated from raw and deconvolved case counts at the state level. We highlight the importance of high-resolution case-based data in understanding biases in disease reporting and how these biases can be avoided by adjusting case numbers based on empirical delay distributions. Code and openly accessible data to reproduce analyses presented here are available.
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Affiliation(s)
- Rhys P.D. Inward
- Department of Biology, University of Oxford, United Kingdom,Corresponding author
| | - Felix Jackson
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | - Abhishek Dasgupta
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | - Graham Lee
- Department of Biology, University of Oxford, United Kingdom,Department of Computer Science, University of Oxford, United Kingdom
| | | | - Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, United Kingdom,NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, United Kingdom
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, United Kingdom,Reuben College, University of Oxford, United Kingdom,Corresponding author at: Department of Biology, University of Oxford, United Kingdom
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