1
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Yu Q, Ascensao JA, Okada T, Boyd O, Volz E, Hallatschek O. Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England. PLoS Pathog 2024; 20:e1012090. [PMID: 38620033 PMCID: PMC11045146 DOI: 10.1371/journal.ppat.1012090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 04/25/2024] [Accepted: 03/03/2024] [Indexed: 04/17/2024] Open
Abstract
Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don't yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.
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Affiliation(s)
- QinQin Yu
- Department of Physics, University of California, Berkeley, California, United States of America
| | - Joao A. Ascensao
- Department of Bioengineering, University of California, Berkeley, California, United States of America
| | - Takashi Okada
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- RIKEN iTHEMS, Wako, Saitama, Japan
| | | | - Olivia Boyd
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Erik Volz
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Oskar Hallatschek
- Department of Physics, University of California, Berkeley, California, United States of America
- Department of Integrative Biology, University of California, Berkeley, California, United States of America
- Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany
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2
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Paredes MI, Ahmed N, Figgins M, Colizza V, Lemey P, McCrone JT, Müller N, Tran-Kiem C, Bedford T. Underdetected dispersal and extensive local transmission drove the 2022 mpox epidemic. Cell 2024; 187:1374-1386.e13. [PMID: 38428425 PMCID: PMC10962340 DOI: 10.1016/j.cell.2024.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/15/2023] [Accepted: 02/02/2024] [Indexed: 03/03/2024]
Abstract
The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.
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Affiliation(s)
- Miguel I Paredes
- Department of Epidemiology, University of Washington, Seattle, WA, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Nashwa Ahmed
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Marlin Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Paris, France
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - John T McCrone
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Nicola Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Cécile Tran-Kiem
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, WA, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Howard Hughes Medical Institute, Seattle, WA, USA
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3
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Faucher B, Sabbatini CE, Czuppon P, Kraemer MUG, Lemey P, Colizza V, Blanquart F, Boëlle PY, Poletto C. Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha. Nat Commun 2024; 15:2152. [PMID: 38461311 PMCID: PMC10925057 DOI: 10.1038/s41467-024-46345-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.
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Affiliation(s)
- Benjamin Faucher
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), F75012, Paris, France
| | - Chiara E Sabbatini
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), F75012, Paris, France
| | - Peter Czuppon
- Institute for Evolution and Biodiversity, University of Münster, Münster, 48149, Germany
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), F75012, Paris, France
- Department of Biology, Georgetown University, Washington, DC, USA
| | - François Blanquart
- Center for Interdisciplinary Research in Biology, CNRS, Collège de France, PSL Research University, Paris, 75005, France
| | - Pierre-Yves Boëlle
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP), F75012, Paris, France
| | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy.
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4
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Novkovic M, Banovic Djeri B, Ristivojevic B, Knezevic A, Jankovic M, Tanasic V, Radojicic V, Keckarevic D, Vidanovic D, Tesovic B, Skakic A, Tolinacki M, Moric I, Djordjevic V. Genome sequence diversity of SARS-CoV-2 in Serbia: insights gained from a 3-year pandemic study. Front Microbiol 2024; 15:1332276. [PMID: 38476954 PMCID: PMC10929721 DOI: 10.3389/fmicb.2024.1332276] [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: 11/03/2023] [Accepted: 01/15/2024] [Indexed: 03/14/2024] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the COVID-19 pandemic, has been evolving rapidly causing emergence of new variants and health uncertainties. Monitoring the evolution of the virus was of the utmost importance for public health interventions and the development of national and global mitigation strategies. Here, we report national data on the emergence of new variants, their distribution, and dynamics in a 3-year study conducted from March 2020 to the end of January 2023 in the Republic of Serbia. Nasopharyngeal and oropharyngeal swabs from 2,398 COVID-19-positive patients were collected and sequenced using three different next generation technologies: Oxford Nanopore, Ion Torrent, and DNBSeq. In the subset of 2,107 SARS-CoV-2 sequences which met the quality requirements, detection of mutations, assignment to SARS-CoV-2 lineages, and phylogenetic analysis were performed. During the 3-year period, we detected three variants of concern, namely, Alpha (5.6%), Delta (7.4%), and Omicron (70.3%) and one variant of interest-Omicron recombinant "Kraken" (XBB1.5) (<1%), whereas 16.8% of the samples belonged to other SARS-CoV-2 (sub)lineages. The detected SARS-CoV-2 (sub)lineages resulted in eight COVID-19 pandemic waves in Serbia, which correspond to the pandemic waves reported in Europe and the United States. Wave dynamics in Serbia showed the most resemblance with the profile of pandemic waves in southern Europe, consistent with the southeastern European location of Serbia. The samples were assigned to sixteen SARS-CoV-2 Nextstrain clades: 20A, 20B, 20C, 20D, 20E, 20G, 20I, 21J, 21K, 21L, 22A, 22B, 22C, 22D, 22E, and 22F and six different Omicron recombinants (XZ, XAZ, XAS, XBB, XBF, and XBK). The 10 most common mutations detected in the coding and untranslated regions of the SARS-CoV-2 genomes included four mutations affecting the spike protein (S:D614G, S:T478K, S:P681H, and S:S477N) and one mutation at each of the following positions: 5'-untranslated region (5'UTR:241); N protein (N:RG203KR); NSP3 protein (NSP3:F106F); NSP4 protein (NSP4:T492I); NSP6 protein (NSP6: S106/G107/F108 - triple deletion), and NSP12b protein (NSP12b:P314L). This national-level study is the most comprehensive in terms of sequencing and genomic surveillance of SARS-CoV-2 during the pandemic in Serbia, highlighting the importance of establishing and maintaining good national practice for monitoring SARS-CoV-2 and other viruses circulating worldwide.
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Affiliation(s)
- Mirjana Novkovic
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Bojana Banovic Djeri
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Bojan Ristivojevic
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Knezevic
- Institute of Microbiology and Immunology, Department of Virology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Marko Jankovic
- Institute of Microbiology and Immunology, Department of Virology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vanja Tanasic
- Center for Forensic and Applied Molecular Genetics, Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | - Verica Radojicic
- Center for Forensic and Applied Molecular Genetics, Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | - Dusan Keckarevic
- Center for Forensic and Applied Molecular Genetics, Faculty of Biology, University of Belgrade, Belgrade, Serbia
| | - Dejan Vidanovic
- Veterinary Specialized Institute “Kraljevo”, Kraljevo, Serbia
| | - Bojana Tesovic
- Veterinary Specialized Institute “Kraljevo”, Kraljevo, Serbia
| | - Anita Skakic
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Maja Tolinacki
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Ivana Moric
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
| | - Valentina Djordjevic
- Center for Genome Sequencing and Bioinformatics, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
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5
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Cianfarini C, Hassler L, Wysocki J, Hassan A, Nicolaescu V, Elli D, Gula H, Ibrahim AM, Randall G, Henkin J, Batlle D. Soluble Angiotensin-Converting Enzyme 2 Protein Improves Survival and Lowers Viral Titers in Lethal Mouse Model of Severe Acute Respiratory Syndrome Coronavirus Type 2 Infection with the Delta Variant. Cells 2024; 13:203. [PMID: 38334597 PMCID: PMC10854654 DOI: 10.3390/cells13030203] [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: 12/22/2023] [Revised: 01/19/2024] [Accepted: 01/20/2024] [Indexed: 02/10/2024] Open
Abstract
Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) utilizes angiotensin-converting enzyme 2 (ACE2) as its main receptor for cell entry. We bioengineered a soluble ACE2 protein termed ACE2 618-DDC-ABD that has increased binding to SARS-CoV-2 and prolonged duration of action. Here, we investigated the protective effect of this protein when administered intranasally to k18-hACE2 mice infected with the aggressive SARS-CoV-2 Delta variant. k18-hACE2 mice were infected with the SARS-CoV-2 Delta variant by inoculation of a lethal dose (2 × 104 PFU). ACE2 618-DDC-ABD (10 mg/kg) or PBS was administered intranasally six hours prior and 24 and 48 h post-viral inoculation. All animals in the PBS control group succumbed to the disease on day seven post-infection (0% survival), whereas, in contrast, there was only one casualty in the group that received ACE2 618-DDC-ABD (90% survival). Mice in the ACE2 618-DDC-ABD group had minimal disease as assessed using a clinical score and stable weight, and both brain and lung viral titers were markedly reduced. These findings demonstrate the efficacy of a bioengineered soluble ACE2 decoy with an extended duration of action in protecting against the aggressive Delta SARS-CoV-2 variant. Together with previous work, these findings underline the universal protective potential against current and future emerging SARS-CoV-2 variants.
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Affiliation(s)
- Cosimo Cianfarini
- Division of Nephrology/Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, 710 North Fairbanks Court, Chicago, IL 60611, USA
- Charité Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Luise Hassler
- Division of Nephrology/Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, 710 North Fairbanks Court, Chicago, IL 60611, USA
| | - Jan Wysocki
- Division of Nephrology/Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, 710 North Fairbanks Court, Chicago, IL 60611, USA
| | - Abdelsabour Hassan
- Division of Nephrology/Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, 710 North Fairbanks Court, Chicago, IL 60611, USA
| | - Vlad Nicolaescu
- Howard Taylor Ricketts Laboratory, Department of Microbiology, The University of Chicago, Lemont, IL 60637, USA
| | - Derek Elli
- Howard Taylor Ricketts Laboratory, Department of Microbiology, The University of Chicago, Lemont, IL 60637, USA
| | - Haley Gula
- Howard Taylor Ricketts Laboratory, Department of Microbiology, The University of Chicago, Lemont, IL 60637, USA
| | - Amany M. Ibrahim
- Howard Taylor Ricketts Laboratory, Department of Microbiology, The University of Chicago, Lemont, IL 60637, USA
| | - Glenn Randall
- Howard Taylor Ricketts Laboratory, Department of Microbiology, The University of Chicago, Lemont, IL 60637, USA
| | - Jack Henkin
- Center for Developmental Therapeutics, Northwestern University, Evanston, IL 60208, USA
| | - Daniel Batlle
- Division of Nephrology/Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, 710 North Fairbanks Court, Chicago, IL 60611, USA
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6
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Sadeghi Mofrad S, Boozarjomehri Amnieh S, Pakzad MR, Zardadi M, Ghazanfari Jajin M, Anvari E, Moghaddam S, Fateh A. The death rate of COVID-19 infection in different SARS-CoV-2 variants was related to C-reactive protein gene polymorphisms. Sci Rep 2024; 14:703. [PMID: 38184750 PMCID: PMC10771501 DOI: 10.1038/s41598-024-51422-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/04/2024] [Indexed: 01/08/2024] Open
Abstract
The serum level of C-reactive protein (CRP) is a significant independent risk factor for Coronavirus disease 2019 (COVID-19). A link was found between serum CRP and genetic diversity within the CRP gene in earlier research. This study examined whether CRP rs1205 and rs1800947 polymorphisms were associated with COVID-19 mortality among various severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) variants. We genotyped CRP rs1205 and rs1800947 polymorphisms in 2023 deceased and 2307 recovered patients using the polymerase chain reaction-restriction fragment length polymorphism method. There was a significant difference between the recovered and the deceased patients in terms of the minor allele frequency of CRP rs1205 T and rs1800947 G. In all three variants, COVID-19 mortality rates were associated with CRP rs1800947 GG genotype. Furthermore, CRP rs1205 CC and rs1800947 GG genotypes showed higher CRP levels. It was found that the G-T haplotype was prevalent in all SARS-CoV-2 variants. The C-C and C-T haplotypes were statistically significant in Delta and Omicron BA.5 variants, respectively. In conclusion, polymorphisms within the CRP gene may relate to serum CRP levels and mortality among COVID-19 patients. In order to verify the utility of CRP polymorphism correlation in predicting COVID-19 mortality, a replication of these results is needed.
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Affiliation(s)
- Sahar Sadeghi Mofrad
- Department of Microbiology, Islamic Azad University of Central Tehran Branch, Tehran, Iran
| | | | - Mohammad Reza Pakzad
- Faculty of Veterinary Medicine, Tabriz Medical Science Branch, Islamic Azad University, Tabriz, Iran
| | - Mina Zardadi
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran
| | | | - Enayat Anvari
- Clinical Research Development Unit, Shahid Mostafa Khomeini Hospital, Ilam University of Medical Science, Ilam, Iran
| | - Sina Moghaddam
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran
| | - Abolfazl Fateh
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran.
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran.
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7
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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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8
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Matteson NL, Hassler GW, Kurzban E, Schwab MA, Perkins SA, Gangavarapu K, Levy JI, Parker E, Pride D, Hakim A, De Hoff P, Cheung W, Castro-Martinez A, Rivera A, Veder A, Rivera A, Wauer C, Holmes J, Wilson J, Ngo SN, Plascencia A, Lawrence ES, Smoot EW, Eisner ER, Tsai R, Chacón M, Baer NA, Seaver P, Salido RA, Aigner S, Ngo TT, Barber T, Ostrander T, Fielding-Miller R, Simmons EH, Zazueta OE, Serafin-Higuera I, Sanchez-Alavez M, Moreno-Camacho JL, García-Gil A, Murphy Schafer AR, McDonald E, Corrigan J, Malone JD, Stous S, Shah S, Moshiri N, Weiss A, Anderson C, Aceves CM, Spencer EG, Hufbauer EC, Lee JJ, King AJ, Ramesh KS, Nguyen KN, Saucedo K, Robles-Sikisaka R, Fisch KM, Gonias SL, Birmingham A, McDonald D, Karthikeyan S, Martin NK, Schooley RT, Negrete AJ, Reyna HJ, Chavez JR, Garcia ML, Cornejo-Bravo JM, Becker D, Isaksson M, Washington NL, Lee W, Garfein RS, Luna-Ruiz Esparza MA, Alcántar-Fernández J, Henson B, Jepsen K, Olivares-Flores B, Barrera-Badillo G, Lopez-Martínez I, Ramírez-González JE, Flores-León R, Kingsmore SF, Sanders A, Pradenas A, White B, Matthews G, Hale M, McLawhon RW, Reed SL, Winbush T, McHardy IH, Fielding RA, Nicholson L, Quigley MM, Harding A, Mendoza A, Bakhtar O, Browne SH, Olivas Flores J, Rincon Rodríguez DG, Gonzalez Ibarra M, Robles Ibarra LC, Arellano Vera BJ, Gonzalez Garcia J, Harvey-Vera A, Knight R, Laurent LC, Yeo GW, Wertheim JO, Ji X, Worobey M, Suchard MA, Andersen KG, Campos-Romero A, Wohl S, Zeller M. Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics. Cell 2023; 186:5690-5704.e20. [PMID: 38101407 PMCID: PMC10795731 DOI: 10.1016/j.cell.2023.11.024] [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: 03/12/2023] [Revised: 08/21/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.
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Affiliation(s)
| | - Gabriel W Hassler
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ezra Kurzban
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Madison A Schwab
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Sarah A Perkins
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik Gangavarapu
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA; Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Joshua I Levy
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Edyth Parker
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - David Pride
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Abbas Hakim
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Peter De Hoff
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Willi Cheung
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; COVID-19 Detection, Investigation, Surveillance, Clinical, and Outbreak Response, California Department of Public Health, Richmond, CA, USA
| | - Anelizze Castro-Martinez
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Andrea Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Anthony Veder
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ariana Rivera
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Cassandra Wauer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jacqueline Holmes
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Jedediah Wilson
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Shayla N Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Ashley Plascencia
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elijah S Lawrence
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Elizabeth W Smoot
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Emily R Eisner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Tsai
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Marisol Chacón
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan A Baer
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Phoebe Seaver
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rodolfo A Salido
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Stefan Aigner
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Toan T Ngo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tom Barber
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Tyler Ostrander
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Rebecca Fielding-Miller
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA; Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | | | - Oscar E Zazueta
- Department of Epidemiology, Secretaria de Salud de Baja California, Tijuana, Baja California, Mexico
| | | | - Manuel Sanchez-Alavez
- Centro de Diagnostico COVID-19 UABC, Tijuana, Baja California, Mexico; Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | | | - Abraham García-Gil
- Clinical Laboratory Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | | | - Eric McDonald
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Jeremy Corrigan
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - John D Malone
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Sarah Stous
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Seema Shah
- County of San Diego Health and Human Services Agency, San Diego, CA, USA
| | - Niema Moshiri
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alana Weiss
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Catelyn Anderson
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Christine M Aceves
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emily G Spencer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Emory C Hufbauer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Justin J Lee
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Alison J King
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Karthik S Ramesh
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kelly N Nguyen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Kieran Saucedo
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | | | - Kathleen M Fisch
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Center for Computational Biology and Bioinformatics, University of California San Diego, La Jolla, CA, USA
| | - Steven L Gonias
- Department of Pathology, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Birmingham
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Smruthi Karthikeyan
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Natasha K Martin
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Robert T Schooley
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA
| | - Agustin J Negrete
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Horacio J Reyna
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose R Chavez
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Maria L Garcia
- Facultad de Ciencias de la Salud Universidad Autonoma de Baja California Valle de Las Palmas, Tijuana, Baja California, Mexico
| | - Jose M Cornejo-Bravo
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico
| | | | | | | | | | - Richard S Garfein
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Benjamin Henson
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Kristen Jepsen
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Beatriz Olivares-Flores
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Gisela Barrera-Badillo
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Irma Lopez-Martínez
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - José E Ramírez-González
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | - Rita Flores-León
- Instituto de Diagnóstico y Referencia Epidemiológicos (InDRE), Ciudad de México, CDMX, Mexico
| | | | - Alison Sanders
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Allorah Pradenas
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Benjamin White
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Gary Matthews
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Matt Hale
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Ronald W McLawhon
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Sharon L Reed
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | - Terri Winbush
- Return to Learn, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | | | | | | | | | - Sara H Browne
- Division of Infectious Disease and Global Public Health, University of California, San Diego, La Jolla, CA, USA; Specialist in Global Health, Encinitas, CA, USA
| | - Jocelyn Olivas Flores
- Facultad de Ciencias Quimicas e Ingenieria, Universidad Autonoma de Baja California, Tijuana, Baja California, Mexico; University of HealthMx, Tijuana, Baja California, Mexico
| | - Diana G Rincon Rodríguez
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Martin Gonzalez Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Facultad de Medicina, Universidad Xochicalco, Tijuana, Baja California, Mexico
| | - Luis C Robles Ibarra
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Tijuana, Baja California, Mexico
| | - Betsy J Arellano Vera
- University of HealthMx, Tijuana, Baja California, Mexico; Instituto Mexicano del Seguro Social, Tijuana, Baja California, Mexico
| | - Jonathan Gonzalez Garcia
- University of HealthMx, Tijuana, Baja California, Mexico; SIMNSA, Tijuana, Baja California, Mexico
| | | | - Rob Knight
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Louise C Laurent
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Sanford Consortium of Regenerative Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Joel O Wertheim
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiang Ji
- Department of Mathematics, School of Science and Engineering, Tulane University, New Orleans, LA, USA
| | - Michael Worobey
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
| | - Marc A Suchard
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kristian G Andersen
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
| | - Abraham Campos-Romero
- Innovation and Research Department, Salud Digna, A.C, Tijuana, Baja California, Mexico
| | - Shirlee Wohl
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Mark Zeller
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
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9
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Paredes MI, Ahmed N, Figgins M, Colizza V, Lemey P, McCrone JT, Müller N, Tran-Kiem C, Bedford T. Early underdetected dissemination across countries followed by extensive local transmission propelled the 2022 mpox epidemic. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.27.23293266. [PMID: 37577709 PMCID: PMC10418578 DOI: 10.1101/2023.07.27.23293266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The World Health Organization declared mpox a public health emergency of international concern in July 2022. To investigate global mpox transmission and population-level changes associated with controlling spread, we built phylogeographic and phylodynamic models to analyze MPXV genomes from five global regions together with air traffic and epidemiological data. Our models reveal community transmission prior to detection, changes in case-reporting throughout the epidemic, and a large degree of transmission heterogeneity. We find that viral introductions played a limited role in prolonging spread after initial dissemination, suggesting that travel bans would have had only a minor impact. We find that mpox transmission in North America began declining before more than 10% of high-risk individuals in the USA had vaccine-induced immunity. Our findings highlight the importance of broader routine specimen screening surveillance for emerging infectious diseases and of joint integration of genomic and epidemiological information for early outbreak control.
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Affiliation(s)
- Miguel I. Paredes
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Nashwa Ahmed
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Marlin Figgins
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP, Paris, France
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - John T. McCrone
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Nicola Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Cécile Tran-Kiem
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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10
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Espinoza B, Adiga A, Venkatramanan S, Warren AS, Chen J, Lewis BL, Vullikanti A, Swarup S, Moon S, Barrett CL, Athreya S, Sundaresan R, Chandru V, Laxminarayan R, Schaffer B, Poor HV, Levin SA, Marathe MV. Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions. Proc Natl Acad Sci U S A 2023; 120:e2305227120. [PMID: 37983514 PMCID: PMC10691339 DOI: 10.1073/pnas.2305227120] [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: 04/04/2023] [Accepted: 10/13/2023] [Indexed: 11/22/2023] Open
Abstract
Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population.
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Affiliation(s)
- Baltazar Espinoza
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Aniruddha Adiga
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Srinivasan Venkatramanan
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Andrew Scott Warren
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Jiangzhuo Chen
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Bryan Leroy Lewis
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Anil Vullikanti
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
| | - Samarth Swarup
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Sifat Moon
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
| | - Christopher Louis Barrett
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
| | - Siva Athreya
- Indian Statistical Institute, Bengaluru, Karnataka560059, India
- International Centre for Theoretical Sciences, Bengaluru, Karnataka560089, India
| | - Rajesh Sundaresan
- Department of Electrical and Communication Engineering, Indian Institute of Science, Bengaluru, Karnataka560012, India
- Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science, Bengaluru, Karnataka560012, India
- Centre for Networked Intelligence, Indian Institute of Science, Bengaluru, Karnataka560012, India
| | - Vijay Chandru
- Strand Life Sciences, Bengaluru, Karnataka560024, India
- BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, Karnataka560012, India
| | | | - Benjamin Schaffer
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
- Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ08544
| | - H. Vincent Poor
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Simon A. Levin
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ08544
| | - Madhav V. Marathe
- Network Systems Science and Advanced Computing Division, Biocomplexity Institute, University of Virginia, Charlottesville, VA22904
- Department of Computer Science, University of Virginia, Charlottesville, VA22904
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11
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Cavallaro M, Dyson L, Tildesley MJ, Todkill D, Keeling MJ. Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis. J R Soc Interface 2023; 20:20230410. [PMID: 37963560 PMCID: PMC10645511 DOI: 10.1098/rsif.2023.0410] [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: 07/19/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Louise Dyson
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Michael J. Tildesley
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
| | - Dan Todkill
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Matt J. Keeling
- School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, UK
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
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12
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Grépin KA, Aston J, Burns J. Effectiveness of international border control measures during the COVID-19 pandemic: a narrative synthesis of published systematic reviews. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20230134. [PMID: 37611627 PMCID: PMC10446907 DOI: 10.1098/rsta.2023.0134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
The effectiveness of international border control measures during the COVID-19 pandemic is not well understood. Using a narrative synthesis approach to published systematic reviews, we synthesized the evidence from both modelling and observational studies on the effects of border control measures on domestic transmission of the virus. We find that symptomatic screening measures were not particularly effective, but that diagnostic-based screening methods were more effective at identifying infected travellers. Targeted travel restrictions levied against travellers from Wuhan were likely temporarily effective but insufficient to stop the exportation of the virus to the rest of the world. Quarantine of inbound travellers was also likely effective at reducing transmission, but only with relatively long quarantine periods, and came with important economic and social effects. There is little evidence that most travel restrictions, including border closure and those implemented to stop the introduction of new variants of concern, were particularly effective. Border control measures played an important role in former elimination locations but only when coupled with strong domestic public health measures. In future outbreaks, if border control measures are to be adopted, they should be seen as part of a broader strategy that includes other non-pharmaceutical interventions. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Karen Ann Grépin
- School of Public Health, University of Hong Kong Faculty of Medicine, Pokfulam, Hong Kong
| | - John Aston
- Statistical Laboratory, University of Cambridge, Cambridge, CB3 0WB, UK
| | - Jacob Burns
- Ludwig-Maximilians University, Munich, 81377, Germany
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13
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Chan YLE, Irvine MA, Prystajecky N, Sbihi H, Taylor M, Joffres Y, Schertzer A, Rose C, Dyson L, Hill EM, Tildesley M, Tyson JR, Hoang LMN, Galanis E. Emergence of SARS-CoV-2 Delta Variant and Effect of Nonpharmaceutical Interventions, British Columbia, Canada. Emerg Infect Dis 2023; 29:1999-2007. [PMID: 37640374 PMCID: PMC10521616 DOI: 10.3201/eid2910.230055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
In British Columbia, Canada, initial growth of the SARS-CoV-2 Delta variant was slower than that reported in other jurisdictions. Delta became the dominant variant (>50% prevalence) within ≈7-13 weeks of first detection in regions within the United Kingdom and United States. In British Columbia, it remained at <10% of weekly incident COVID-19 cases for 13 weeks after first detection on March 21, 2021, eventually reaching dominance after 17 weeks. We describe the growth of Delta variant cases in British Columbia during March 1-June 30, 2021, and apply retrospective counterfactual modeling to examine factors for the initially low COVID-19 case rate after Delta introduction, such as vaccination coverage and nonpharmaceutical interventions. Growth of COVID-19 cases in the first 3 months after Delta emergence was likely limited in British Columbia because additional nonpharmaceutical interventions were implemented to reduce levels of contact at the end of March 2021, soon after variant emergence.
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14
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Xie R, Edwards KM, Wille M, Wei X, Wong SS, Zanin M, El-Shesheny R, Ducatez M, Poon LLM, Kayali G, Webby RJ, Dhanasekaran V. The episodic resurgence of highly pathogenic avian influenza H5 virus. Nature 2023; 622:810-817. [PMID: 37853121 DOI: 10.1038/s41586-023-06631-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 09/11/2023] [Indexed: 10/20/2023]
Abstract
Highly pathogenic avian influenza (HPAI) H5N1 activity has intensified globally since 2021, increasingly causing mass mortality in wild birds and poultry and incidental infections in mammals1-3. However, the ecological and virological properties that underscore future mitigation strategies still remain unclear. Using epidemiological, spatial and genomic approaches, we demonstrate changes in the origins of resurgent HPAI H5 and reveal significant shifts in virus ecology and evolution. Outbreak data show key resurgent events in 2016-2017 and 2020-2021, contributing to the emergence and panzootic spread of H5N1 in 2021-2022. Genomic analysis reveals that the 2016-2017 epizootics originated in Asia, where HPAI H5 reservoirs are endemic. In 2020-2021, 2.3.4.4b H5N8 viruses emerged in African poultry, featuring mutations altering HA structure and receptor binding. In 2021-2022, a new H5N1 virus evolved through reassortment in wild birds in Europe, undergoing further reassortment with low-pathogenic avian influenza in wild and domestic birds during global dissemination. These results highlight a shift in the HPAI H5 epicentre beyond Asia and indicate that increasing persistence of HPAI H5 in wild birds is facilitating geographic and host range expansion, accelerating dispersion velocity and increasing reassortment potential. As earlier outbreaks of H5N1 and H5N8 were caused by more stable genomic constellations, these recent changes reflect adaptation across the domestic-bird-wild-bird interface. Elimination strategies in domestic birds therefore remain a high priority to limit future epizootics.
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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, 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, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Michelle Wille
- Sydney Institute for Infectious Diseases, School of Medical Sciences, The University of Sydney, Sydney, New South Wales, Australia
- Department of Microbiology and Immunology, at the Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, Victoria, Australia
| | - Xiaoman Wei
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Sook-San Wong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Mark Zanin
- 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, Hong Kong SAR, China
| | - Rabeh El-Shesheny
- Center of Scientific Excellence for Influenza Viruses, National Research Centre, Giza, Egypt
| | - Mariette Ducatez
- IHAP, Université de Toulouse, Institut national de recherche pour l'agriculture, l'alimentation et l'environnement, Ecole Nationale Vétérinaire de Toulouse, Toulouse, France
| | - 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, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | | | - Richard J Webby
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Vijaykrishna Dhanasekaran
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- HKU-Pasteur Research Pole, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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15
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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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16
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Scheuermann SE, Goff K, Rowe LA, Beddingfield BJ, Maness NJ. Real-Time Analysis of SARS-CoV-2-Induced Cytolysis Reveals Distinct Variant-Specific Replication Profiles. Viruses 2023; 15:1937. [PMID: 37766343 PMCID: PMC10537736 DOI: 10.3390/v15091937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/31/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
The ability of each new SARS-CoV-2 variant to evade host humoral immunity is the focus of intense research. Each variant may also harbor unique replication capabilities relevant for disease and transmission. Here, we demonstrate a new approach to assessing viral replication kinetics using real-time cell analysis (RTCA). Virus-induced cell death is measured in real time as changes in electrical impedance through cell monolayers while images are acquired at defined intervals via an onboard microscope and camera. Using this system, we quantified replication kinetics of five clinically important viral variants: WA1/2020 (ancestral), Delta, and Omicron subvariants BA.1, BA.4, and BA.5. Multiple measures proved useful in variant replication comparisons, including the elapsed time to, and the slope at, the maximum rate of cell death. Important findings include significantly weaker replication kinetics of BA.1 by all measures, while BA.5 harbored replication kinetics at or near ancestral levels, suggesting evolution to regain replicative capacity, and both an altered profile of cell killing and enhanced fusogenicity of the Delta variant. Together, these data show that RTCA is a robust method to assess replicative capacity of any given SARS-CoV-2 variant rapidly and quantitatively, which may be useful in assessment of newly emerging variants.
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Affiliation(s)
- Sarah E. Scheuermann
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA 70433, USA; (S.E.S.); (K.G.); (L.A.R.)
| | - Kelly Goff
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA 70433, USA; (S.E.S.); (K.G.); (L.A.R.)
| | - Lori A. Rowe
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA 70433, USA; (S.E.S.); (K.G.); (L.A.R.)
| | - Brandon J. Beddingfield
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA 70433, USA; (S.E.S.); (K.G.); (L.A.R.)
- Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Nicholas J. Maness
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA 70433, USA; (S.E.S.); (K.G.); (L.A.R.)
- Department of Microbiology and Immunology, Tulane University School of Medicine, New Orleans, LA 70112, USA
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17
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Yang L, Wang Z, Wang L, Vrancken B, Wang R, Wei Y, Rader B, Wu CH, Chen Y, Wu P, Li B, Lin Q, Dong L, Cui Y, Shi M, Brownstein JS, Stenseth NC, Yang R, Tian H. Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2. Proc Natl Acad Sci U S A 2023; 120:e2305403120. [PMID: 37549270 PMCID: PMC10434302 DOI: 10.1073/pnas.2305403120] [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: 04/06/2023] [Accepted: 07/07/2023] [Indexed: 08/09/2023] Open
Abstract
Continually emerging SARS-CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID-19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID-19 vaccine rollout, SARS-CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country-to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.
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Affiliation(s)
- Lingyue Yang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Zengmiao Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, CambridgeCB2 3EH, United Kingdom
| | - Bram Vrancken
- Department of Microbiology and Immunology, Rega Institute, Laboratory of Evolutionary and Computational Virology, KU Leuven, Leuven3000, Belgium
- Spatial Epidemiology Lab, Université Libre de Bruxelles, 1050Bruxelles, Belgium
| | - Ruixue Wang
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Yuanlong Wei
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children’s Hospital, Boston, MA02215
- Department of Epidemiology, Boston University School of Public Health, Boston, MA02118
| | - Chieh-Hsi Wu
- Mathematical Sciences, University of Southampton, SouthamptonSO17 1BJ, United Kingdom
| | - Yuyang Chen
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Peiyi Wu
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Bingying Li
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Qiushi Lin
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
| | - Lu Dong
- College of Life Sciences, Beijing Normal University, Beijing100875, China
| | - Yujun Cui
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing100071, China
| | - Mang Shi
- The Centre for Infection and Immunity Studies, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen518107, China
| | - John S. Brownstein
- Spatial Epidemiology Lab, Université Libre de Bruxelles, 1050Bruxelles, Belgium
- Harvard Medical School, Harvard University, Boston, MA02115
| | - Nils Chr. Stenseth
- The Centre for Pandemics and One-Health Research, Sustainable Health Unit, Faculty of Medicine, University of Oslo, Oslo0316, Norway
- Centre for Ecological and Evolutionary Synthesis, Department of Biosciences, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo0316, Norway
- Vanke School of Public Health, Tsinghua University, Beijing100084, China
| | - Ruifu Yang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing100071, China
| | - Huaiyu Tian
- State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing100875, China
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18
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Li K, Melnychuk S, Sandstrom P, Ji H. Tracking the evolution of the SARS-CoV-2 Delta variant of concern: analysis of genetic diversity and selection across the whole viral genome. Front Microbiol 2023; 14:1222301. [PMID: 37614597 PMCID: PMC10443222 DOI: 10.3389/fmicb.2023.1222301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/17/2023] [Indexed: 08/25/2023] Open
Abstract
Background Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has diversified extensively, producing five highly virulent lineages designated as variants of concern (VOCs). The Delta VOC emerged in India with increased transmission, immune evasion, and mortality, causing a massive global case surge in 2021. This study aims to understand how the Delta VOC evolved by characterizing mutation patterns in the viral population before and after its emergence. Furthermore, we aim to identify the influence of positive and negative selection on VOC evolution and understand the prevalence of different mutation types in the viral genome. Methods Three groups of whole viral genomes were retrieved from GISAID, sourced from India, with collection periods as follows: Group A-during the initial appearance of SARS-CoV-2; Group B-just before the emergence of the Delta variant; Group C-after the establishment of the Delta variant in India. Mutations in >1% of each group were identified with BioEdit to reveal differences in mutation quantity and type. Sites under positive or negative selection were identified with FUBAR. The results were compared to determine how mutations correspond with selective pressures and how viral mutation profiles changed to reflect genetic diversity before and after VOC emergence. Results The number of mutations increased progressively in Groups A-C, with Group C reporting a 2.2- and 1.9-fold increase from Groups A and B, respectively. Among all the observed mutations, Group C had the highest percentage of deletions (22.7%; vs. 4.2% and 2.6% in Groups A and B, respectively), and most mutations altered the final amino acid code, such as non-synonymous substitutions and deletions. Conversely, Group B had the most synonymous substitutions that are effectively silent. The number of sites experiencing positive selection increased in Groups A-C, but Group B had 2.4- and 2.6 times more sites under negative selection compared to Groups A and C, respectively. Conclusion Our findings demonstrated that viral genetic diversity continuously increased during and after the emergence of the Delta VOC. Despite this, Group B reports heightened negative selection, which potentially preserves important gene regions during evolution. Group C contains an unprecedented quantity of mutations and positively selected sites, providing strong evidence of active viral adaptation in the population.
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Affiliation(s)
- Katherine Li
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Stephanie Melnychuk
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Paul Sandstrom
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| | - Hezhao Ji
- National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, MB, Canada
- Department of Medical Microbiology and Infectious Diseases, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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19
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Pascall DJ, Vink E, Blacow R, Bulteel N, Campbell A, Campbell R, Clifford S, Davis C, da Silva Filipe A, El Sakka N, Fjodorova L, Forrest R, Goldstein E, Gunson R, Haughney J, Holden MTG, Honour P, Hughes J, James E, Lewis T, MacLean O, McHugh M, Mollett G, Nyberg T, Onishi Y, Parcell B, Ray S, Robertson DL, Seaman SR, Shabaan S, Shepherd JG, Smollett K, Templeton K, Wastnedge E, Wilkie C, Williams T, Thomson EC. Directions of change in intrinsic case severity across successive SARS-CoV-2 variant waves have been inconsistent. J Infect 2023; 87:128-135. [PMID: 37270070 PMCID: PMC10234362 DOI: 10.1016/j.jinf.2023.05.019] [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: 09/26/2022] [Revised: 03/27/2023] [Accepted: 05/24/2023] [Indexed: 06/05/2023]
Abstract
OBJECTIVES To determine how the intrinsic severity of successively dominant SARS-CoV-2 variants changed over the course of the pandemic. METHODS A retrospective cohort analysis in the NHS Greater Glasgow and Clyde (NHS GGC) Health Board. All sequenced non-nosocomial adult COVID-19 cases in NHS GGC with relevant SARS-CoV-2 lineages (B.1.177/Alpha, Alpha/Delta, AY.4.2 Delta/non-AY.4.2 Delta, non-AY.4.2 Delta/Omicron, and BA.1 Omicron/BA.2 Omicron) during analysis periods were included. Outcome measures were hospital admission, ICU admission, or death within 28 days of positive COVID-19 test. We report the cumulative odds ratio; the ratio of the odds that an individual experiences a severity event of a given level vs all lower severity levels for the resident and the replacement variant after adjustment. RESULTS After adjustment for covariates, the cumulative odds ratio was 1.51 (95% CI: 1.08-2.11) for Alpha versus B.1.177, 2.09 (95% CI: 1.42-3.08) for Delta versus Alpha, 0.99 (95% CI: 0.76-1.27) for AY.4.2 Delta versus non-AY.4.2 Delta, 0.49 (95% CI: 0.22-1.06) for Omicron versus non-AY.4.2 Delta, and 0.86 (95% CI: 0.68-1.09) for BA.2 Omicron versus BA.1 Omicron. CONCLUSIONS The direction of change in intrinsic severity between successively emerging SARS-CoV-2 variants was inconsistent, reminding us that the intrinsic severity of future SARS-CoV-2 variants remains uncertain.
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Affiliation(s)
- David J Pascall
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom; Joint Universities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom.
| | - Elen Vink
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom; NHS Lothian, Edinburgh EH1 3EG, United Kingdom.
| | - Rachel Blacow
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom; NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom.
| | | | | | | | | | - Chris Davis
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | - Ana da Silva Filipe
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | | | | | | | - Emily Goldstein
- NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom.
| | - Rory Gunson
- NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom.
| | - John Haughney
- NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom.
| | - Matthew T G Holden
- Public Health Scotland, Edinburgh EH12 9EB, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom.
| | | | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | | | - Tim Lewis
- NHS Lothian, Edinburgh EH1 3EG, United Kingdom.
| | - Oscar MacLean
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | | | - Guy Mollett
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom; NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom.
| | - Tommy Nyberg
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom.
| | | | - Ben Parcell
- School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom.
| | - Surajit Ray
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8TA, United Kingdom.
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | - Shaun R Seaman
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, United Kingdom.
| | - Sharif Shabaan
- Public Health Scotland, Edinburgh EH12 9EB, United Kingdom.
| | - James G Shepherd
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | - Katherine Smollett
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom.
| | | | | | - Craig Wilkie
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8TA, United Kingdom.
| | - Thomas Williams
- NHS Lothian, Edinburgh EH1 3EG, United Kingdom; Royal Hospital for Children and Young People, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom.
| | - Emma C Thomson
- MRC-University of Glasgow Centre for Virus Research (CVR), Glasgow G61 1QH, United Kingdom; NHS Greater Glasgow and Clyde, Glasgow G12 0XH, United Kingdom; London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom.
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20
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Tsui JLH, McCrone JT, Lambert B, Bajaj S, Inward RP, Bosetti P, Tegally H, Hill V, Pena RE, Zarebski AE, Peacock TP, Liu L, Wu N, Davis M, Bogoch II, Khan K, Kall M, Abdul Aziz NIB, Colquhoun R, O’Toole Á, Jackson B, Dasgupta A, Wilkinson E, de Oliveira T, Connor TR, Loman NJ, Colizza V, Fraser C, Volz E, Ji X, Gutierrez B, Chand M, Dellicour S, Cauchemez S, Raghwani J, Suchard MA, Lemey P, Rambaut A, Pybus OG, Kraemer MU. Genomic assessment of invasion dynamics of SARS-CoV-2 Omicron BA.1. Science 2023; 381:336-343. [PMID: 37471538 PMCID: PMC10866301 DOI: 10.1126/science.adg6605] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) now arise in the context of heterogeneous human connectivity and population immunity. Through a large-scale phylodynamic analysis of 115,622 Omicron BA.1 genomes, we identified >6,000 introductions of the antigenically distinct VOC into England and analyzed their local transmission and dispersal history. We find that six of the eight largest English Omicron lineages were already transmitting when Omicron was first reported in southern Africa (22 November 2021). Multiple datasets show that importation of Omicron continued despite subsequent restrictions on travel from southern Africa as a result of export from well-connected secondary locations. Initiation and dispersal of Omicron transmission lineages in England was a two-stage process that can be explained by models of the country's human geography and hierarchical travel network. Our results enable a comparison of the processes that drive the invasion of Omicron and other VOCs across multiple spatial scales.
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Affiliation(s)
| | - John T. McCrone
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Helix, San Mateo, USA
| | - Ben Lambert
- Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK
| | - Sumali Bajaj
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Paolo Bosetti
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Verity Hill
- Helix, San Mateo, USA
- Yale University, New Haven, USA
| | | | | | - Thomas P. Peacock
- Department of Infectious Disease, Imperial College London, London, UK
- UK Health Security Agency, London, UK
| | | | - Neo Wu
- Google Research, Mountain View, USA
| | | | - Isaac I. Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | | | | | | | | | | | - Eduan Wilkinson
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Thomas R. Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, The Sir Martin Evans Building, Cardiff University, UK
- Quadram Institute, Norwich, UK
| | - Nicholas J. Loman
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP), Paris, France
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
| | - Erik Volz
- MRC Centre of Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Xiang Ji
- Department of Mathematics, Tulane University, New Orleans, USA
| | | | | | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | - Simon Cauchemez
- Institut Pasteur, Université Paris Cité, CNRS, Paris, France
| | - Jayna Raghwani
- Department of Biology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Marc A. Suchard
- Departments of Biostatistics, Biomathematics and Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium
| | | | - Oliver G. Pybus
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
- Department of Pathobiology and Population Science, Royal Veterinary College, London, UK
| | - Moritz U.G. Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, UK
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21
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Tegally H, Wilkinson E, Tsui JLH, Moir M, Martin D, Brito AF, Giovanetti M, Khan K, Huber C, Bogoch II, San JE, Poongavanan J, Xavier JS, Candido DDS, Romero F, Baxter C, Pybus OG, Lessells RJ, Faria NR, Kraemer MUG, de Oliveira T. Dispersal patterns and influence of air travel during the global expansion of SARS-CoV-2 variants of concern. Cell 2023; 186:3277-3290.e16. [PMID: 37413988 PMCID: PMC10247138 DOI: 10.1016/j.cell.2023.06.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 07/08/2023]
Abstract
The Alpha, Beta, and Gamma SARS-CoV-2 variants of concern (VOCs) co-circulated globally during 2020 and 2021, fueling waves of infections. They were displaced by Delta during a third wave worldwide in 2021, which, in turn, was displaced by Omicron in late 2021. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of VOCs worldwide. We find that source-sink dynamics varied substantially by VOC and identify countries that acted as global and regional hubs of dissemination. We demonstrate the declining role of presumed origin countries of VOCs in their global dispersal, estimating that India contributed <15% of Delta exports and South Africa <1%-2% of Omicron dispersal. We estimate that >80 countries had received introductions of Omicron within 100 days of its emergence, associated with accelerated passenger air travel and higher transmissibility. Our study highlights the rapid dispersal of highly transmissible variants, with implications for genomic surveillance along the hierarchical airline network.
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Affiliation(s)
- Houriiyah Tegally
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa; KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa.
| | - Eduan Wilkinson
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Monika Moir
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Darren Martin
- Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Cape Town, South Africa; Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | - Marta Giovanetti
- Laboratorio de Flavivirus, Fundacao Oswaldo Cruz, Rio de Janeiro, Brazil; Laboratório de Genética Celular e Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, Italy
| | - Kamran Khan
- BlueDot, Toronto, ON, Canada; Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
| | | | - Isaac I Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, Canada
| | - James Emmanuel San
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Jenicca Poongavanan
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Joicymara S Xavier
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa; Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Unaí, Brazil
| | - Darlan da S Candido
- MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Filipe Romero
- MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Cheryl Baxter
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Oliver G Pybus
- Department of Biology, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK; Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
| | - Richard J Lessells
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Nuno R Faria
- Department of Biology, University of Oxford, Oxford, UK; MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK; Departamento de Moléstias Infecciosas e Parasitárias e Instituto de Medicina Tropical da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK.
| | - Tulio de Oliveira
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa; KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa; Department of Global Health, University of Washington, Seattle, WA, USA.
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22
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Janitra FE, Jen HJ, Chu H, Chen R, Pien LC, Liu D, Lai YJ, Banda KJ, Lee TY, Lin HC, Chang CY, Chou KR. Global prevalence of low resilience among the general population and health professionals during the COVID-19 pandemic: A meta-analysis. J Affect Disord 2023; 332:29-46. [PMID: 37004902 PMCID: PMC10063525 DOI: 10.1016/j.jad.2023.03.077] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/10/2023] [Accepted: 03/24/2023] [Indexed: 04/03/2023]
Abstract
Objective To estimate the global prevalence of low resilience among the general population and health professionals during the COVID-19 pandemic. Methods Embase, Ovid-MEDLINE, PubMed, Scopus, Web of Science, CINAHL, WHO COVID-19 databases, and grey literature were searched for studies from January 1, 2020, to August 22, 2022. Hoy's assessment tool was used to assess for risk of bias. Meta-analysis and moderator analysis was performed using the Generalized Linear Mixed Model with a corresponding 95 % confidence interval (95 % CI) adopting the random-effect model in R software. Between-study heterogeneity was measured using I2 and τ2 statistics. Results Overall, 44 studies involving 51,119 participants were identified. The pooled prevalence of low resilience was 27.0 % (95 % CI: 21.0 %–33.0 %) with prevalence among the general population being 35.0 % (95 % CI: 28.0 %–42.0 %) followed by 23.0 % (95 % CI: 16.0 %–30.9 %) for health professionals. The 3-month trend analysis of the prevalence of low resilience beginning January 2020 to June 2021 revealed upward then downward patterns among overall populations. The prevalence of low resilience was higher in females, studied during the delta variant dominant period, frontline health professionals, and undergraduate degree education. Limitations Study outcomes showed high heterogeneity; however, sub-group and meta-regression analyses were conducted to identify potential moderating factors. Conclusions Globally, 1 out of 4 people among the general population and health professionals experienced low resilience due to COVID-19 adversity. The prevalence of low resilience was twice as much among the general population compared to health professionals. These findings provide information for policymakers and clinicians in the development and implementation of resilience-enhancing programs.
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Affiliation(s)
- Fitria Endah Janitra
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Faculty of Nursing, Universitas Islam Sultan Agung, Semarang, Indonesia
| | - Hsiu-Ju Jen
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
| | - Hsin Chu
- Institute of Aerospace and Undersea Medicine, School of Medicine, National Defense Medical Center, Taipei, Taiwan; Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ruey Chen
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan; Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan
| | - Li-Chung Pien
- Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Doresses Liu
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Research Center in Nursing Clinical Practice, Wan Fang Hospital Taipei Medical University, Taipei, Taiwan
| | - Yueh-Jung Lai
- Department of Nursing, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Kondwani Joseph Banda
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Endoscopy Unit, Surgery Department, Kamuzu Central Hospital, Lilongwe, Malawi
| | - Tso-Ying Lee
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Nursing Research Center, Department of Nursing, Taipei Medical University Hospital, Taipei, Taiwan
| | - Hui-Chen Lin
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Research Center in Nursing Clinical Practice, Wan Fang Hospital Taipei Medical University, Taipei, Taiwan
| | - Ching-Yi Chang
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Post-Baccalaureate Program in Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan
| | - Kuei-Ru Chou
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan; Research Center in Nursing Clinical Practice, Wan Fang Hospital Taipei Medical University, Taipei, Taiwan; Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan; Neuroscience Research Center, Taipei Medical University, Taipei, Taiwan.
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23
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Orf GS, Pérez LJ, Ciuoderis K, Cardona A, Villegas S, Hernández-Ortiz JP, Baele G, Mohaimani A, Osorio JE, Berg MG, Cloherty GA. The Principles of SARS-CoV-2 Intervariant Competition Are Exemplified in the Pre-Omicron Era of the Colombian Epidemic. Microbiol Spectr 2023; 11:e0534622. [PMID: 37191534 PMCID: PMC10269686 DOI: 10.1128/spectrum.05346-22] [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: 12/28/2022] [Accepted: 04/25/2023] [Indexed: 05/17/2023] Open
Abstract
The first 18 months of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Colombia were characterized by three epidemic waves. During the third wave, from March through August 2021, intervariant competition resulted in Mu replacing Alpha and Gamma. We employed Bayesian phylodynamic inference and epidemiological modeling to characterize the variants in the country during this period of competition. Phylogeographic analysis indicated that Mu did not emerge in Colombia but acquired increased fitness there through local transmission and diversification, contributing to its export to North America and Europe. Despite not having the highest transmissibility, Mu's genetic composition and ability to evade preexisting immunity facilitated its domination of the Colombian epidemic landscape. Our results support previous modeling studies demonstrating that both intrinsic factors (transmissibility and genetic diversity) and extrinsic factors (time of introduction and acquired immunity) influence the outcome of intervariant competition. This analysis will help set practical expectations about the inevitable emergences of new variants and their trajectories. IMPORTANCE Before the appearance of the Omicron variant in late 2021, numerous SARS-CoV-2 variants emerged, were established, and declined, often with different outcomes in different geographic areas. In this study, we considered the trajectory of the Mu variant, which only successfully dominated the epidemic landscape of a single country: Colombia. We demonstrate that Mu competed successfully there due to its early and opportune introduction time in late 2020, combined with its ability to evade immunity granted by prior infection or the first generation of vaccines. Mu likely did not effectively spread outside of Colombia because other immune-evading variants, such as Delta, had arrived in those locales and established themselves first. On the other hand, Mu's early spread within Colombia may have prevented the successful establishment of Delta there. Our analysis highlights the geographic heterogeneity of early SARS-CoV-2 variant spread and helps to reframe the expectations for the competition behaviors of future variants.
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Affiliation(s)
- Gregory S. Orf
- Infectious Disease Research, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, Illinois, USA
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
| | - Lester J. Pérez
- Infectious Disease Research, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, Illinois, USA
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
| | - Karl Ciuoderis
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
- UW-GHI One Health Colombia, Universidad Nacional de Colombia Sede en Medellín, Medellín, Colombia
| | - Andrés Cardona
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
- UW-GHI One Health Colombia, Universidad Nacional de Colombia Sede en Medellín, Medellín, Colombia
| | - Simón Villegas
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
- UW-GHI One Health Colombia, Universidad Nacional de Colombia Sede en Medellín, Medellín, Colombia
| | - Juan P. Hernández-Ortiz
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
- UW-GHI One Health Colombia, Universidad Nacional de Colombia Sede en Medellín, Medellín, Colombia
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Laboratory of Clinical and Evolutionary Virology, Rega Institute, KU Leuven, Leuven, Belgium
| | - Aurash Mohaimani
- Infectious Disease Research, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, Illinois, USA
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
| | - Jorge E. Osorio
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
- UW-GHI One Health Colombia, Universidad Nacional de Colombia Sede en Medellín, Medellín, Colombia
- UW-GHI One Health Colombia, University of Wisconsin—Madison, Madison, Wisconsin, USA
| | - Michael G. Berg
- Infectious Disease Research, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, Illinois, USA
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
| | - Gavin A. Cloherty
- Infectious Disease Research, Abbott Diagnostics Division, Abbott Laboratories, Abbott Park, Illinois, USA
- Abbott Pandemic Defense Coalition (APDC), Abbott Park, Illinois, USA
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24
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Yao XA, Crooks A, Jiang B, Krisp J, Liu X, Huang H. An overview of urban analytical approaches to combating the Covid-19 pandemic. ENVIRONMENT AND PLANNING. B, URBAN ANALYTICS AND CITY SCIENCE 2023; 50:1133-1143. [PMID: 38602958 PMCID: PMC10160829 DOI: 10.1177/23998083231174748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Affiliation(s)
- X Angela Yao
- Department of Geography, University of Georgia, Athens, GA, USA
| | - Andrew Crooks
- Department of Geography, University at Buffalo, Buffalo, NY, USA
| | - Bin Jiang
- Urban Governance and Design Thrust, The Hong Kong University of Science and Technology, Guangzhou, China
| | - Jukka Krisp
- Institute of Geography, Applied Geoinformatics, Augsburg University, Germany
| | - Xintao Liu
- Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR
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25
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Cheng Y, Ji C, Zhou HY, Zheng H, Wu A. Web Resources for SARS-CoV-2 Genomic Database, Annotation, Analysis and Variant Tracking. Viruses 2023; 15:v15051158. [PMID: 37243244 DOI: 10.3390/v15051158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
The SARS-CoV-2 genomic data continue to grow, providing valuable information for researchers and public health officials. Genomic analysis of these data sheds light on the transmission and evolution of the virus. To aid in SARS-CoV-2 genomic analysis, many web resources have been developed to store, collate, analyze, and visualize the genomic data. This review summarizes web resources used for the SARS-CoV-2 genomic epidemiology, covering data management and sharing, genomic annotation, analysis, and variant tracking. The challenges and further expectations for these web resources are also discussed. Finally, we highlight the importance and need for continued development and improvement of related web resources to effectively track the spread and understand the evolution of the virus.
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Affiliation(s)
- Yexiao Cheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Chengyang Ji
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Hang-Yu Zhou
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
| | - Heng Zheng
- School of Life Science and Technology, China Pharmaceutical University, Nanjing 211100, China
| | - Aiping Wu
- Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China
- Suzhou Institute of Systems Medicine, Suzhou 215123, China
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26
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Salzberger B, Mellmann A, Bludau A, Ciesek S, Corman V, Dilthey A, Donker T, Eckmanns T, Egelkamp R, Gatermann SG, Grundmann H, Häcker G, Kaase M, Lange B, Mielke M, Pletz MW, Semmler T, Thürmer A, Wieler LH, Wolff T, Widmer AF, Scheithauer S. An appeal for strengthening genomic pathogen surveillance to improve pandemic preparedness and infection prevention: the German perspective. Infection 2023:10.1007/s15010-023-02040-9. [PMID: 37129842 PMCID: PMC10152431 DOI: 10.1007/s15010-023-02040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
The SARS-CoV-2 pandemic has highlighted the importance of viable infection surveillance and the relevant infrastructure. From a German perspective, an integral part of this infrastructure, genomic pathogen sequencing, was at best fragmentary and stretched to its limits due to the lack or inefficient use of equipment, human resources, data management and coordination. The experience in other countries has shown that the rate of sequenced positive samples and linkage of genomic and epidemiological data (person, place, time) represent important factors for a successful application of genomic pathogen surveillance. Planning, establishing and consistently supporting adequate structures for genomic pathogen surveillance will be crucial to identify and combat future pandemics as well as other challenges in infectious diseases such as multi-drug resistant bacteria and healthcare-associated infections. Therefore, the authors propose a multifaceted and coordinated process for the definition of procedural, legal and technical standards for comprehensive genomic pathogen surveillance in Germany, covering the areas of genomic sequencing, data collection and data linkage, as well as target pathogens. A comparative analysis of the structures established in Germany and in other countries is applied. This proposal aims to better tackle epi- and pandemics to come and take action from the "lessons learned" from the SARS-CoV-2 pandemic.
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Affiliation(s)
- Bernd Salzberger
- Department for Infection Control and Infectious Diseases, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
| | - Alexander Mellmann
- Institute for Hygiene, University Hospital Münster, Robert-Koch-Straße 41, 48149, Münster, Germany.
| | - Anna Bludau
- Department for Infection Control and Infectious Diseases, University Medical Center (UMG), Georg-August University Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Sandra Ciesek
- Institute of Medical Virology, University Hospital Frankfurt, Goethe University, Frankfurt Am Main, Germany
| | - Victor Corman
- Institute of Virology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Alexander Dilthey
- Institute of Medical Microbiology and Hospital Hygiene, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Tjibbe Donker
- Institute for Infection Prevention and Hospital Epidemiology, University Medical Center Freiburg, Freiburg, Germany
| | | | - Richard Egelkamp
- Next Generation Sequencing, Public Health Agency of Lower Saxony, Hanover, Germany
| | - Sören G Gatermann
- Department of Medical Microbiology, Ruhr University Bochum, Bochum, Germany
| | - Hajo Grundmann
- Institute for Infection Prevention and Hospital Epidemiology, University Medical Center Freiburg, Freiburg, Germany
| | - Georg Häcker
- Faculty of Medicine, Institute of Medical Microbiology and Hygiene, Medical Centre University of Freiburg, Freiburg, Germany
| | - Martin Kaase
- Department for Infection Control and Infectious Diseases, University Medical Center (UMG), Georg-August University Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany
| | - Berit Lange
- Department of Epidemiology, Helmholtz Centre for Infection Research, Brunswick, Germany
| | | | - Mathias W Pletz
- Institute of Infectious Diseases and Infection Control, University Hospital, Jena, Germany
| | | | | | | | | | - Andreas F Widmer
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Basel, Switzerland
| | - Simone Scheithauer
- Department for Infection Control and Infectious Diseases, University Medical Center (UMG), Georg-August University Göttingen, Robert-Koch-Straße 40, 37075, Göttingen, Germany.
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27
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Petrone ME, Lucas C, Menasche B, Breban MI, Yildirim I, Campbell M, Omer SB, Holmes EC, Ko AI, Grubaugh ND, Iwasaki A, Wilen CB, Vogels CBF, Fauver JR. Nonsystematic Reporting Biases of the SARS-CoV-2 Variant Mu Could Impact Our Understanding of the Epidemiological Dynamics of Emerging Variants. Genome Biol Evol 2023; 15:evad052. [PMID: 36974986 PMCID: PMC10113931 DOI: 10.1093/gbe/evad052] [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: 11/21/2022] [Revised: 02/16/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Developing a timely and effective response to emerging SARS-CoV-2 variants of concern (VOCs) is of paramount public health importance. Global health surveillance does not rely on genomic data alone to identify concerning variants when they emerge. Instead, methods that utilize genomic data to estimate the epidemiological dynamics of emerging lineages have the potential to serve as an early warning system. However, these methods assume that genomic data are uniformly reported across circulating lineages. In this study, we analyze differences in reporting delays among SARS-CoV-2 VOCs as a plausible explanation for the timing of the global response to the former VOC Mu. Mu likely emerged in South America in mid-2020, where its circulation was largely confined. In this study, we demonstrate that Mu was designated as a VOC ∼1 year after it emerged and find that the reporting of genomic data for Mu differed significantly than that of other VOCs within countries, states, and individual laboratories. Our findings suggest that nonsystematic biases in the reporting of genomic data may have delayed the global response to Mu. Until they are resolved, the surveillance gaps that affected the global response to Mu could impede the rapid and accurate assessment of future emerging variants.
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Affiliation(s)
- Mary E Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Sydney Institute for Infectious Diseases, School of Medical Sciences, University of Sydney, NSW, Australia
| | - Carolina Lucas
- Department of Immunobiology, Yale University School of Medicine
| | - Bridget Menasche
- Department of Laboratory Medicine, Yale University School of Medicine
| | - Mallery I Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
| | - Inci Yildirim
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Department of Pediatric, Section of Infectious Diseases and Global Health, Yale University School of Medicine
- Yale Institute for Global Health, Yale University
| | - Melissa Campbell
- Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine
| | - Saad B Omer
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Yale Institute for Global Health, Yale University
- Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine
| | - Edward C Holmes
- Sydney Institute for Infectious Diseases, School of Medical Sciences, University of Sydney, NSW, Australia
| | - Albert I Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine
| | - Nathan D Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Department of Ecology and Evolutionary Biology, Yale University
| | - Akiko Iwasaki
- Department of Immunobiology, Yale University School of Medicine
- Howard Hughes Medical Institute
| | - Craig B Wilen
- Department of Immunobiology, Yale University School of Medicine
- Department of Laboratory Medicine, Yale University School of Medicine
| | - Chantal B F Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
| | - Joseph R Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- College of Public Health, University of Nebraska Medical Center
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28
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Markov PV, Ghafari M, Beer M, Lythgoe K, Simmonds P, Stilianakis NI, Katzourakis A. The evolution of SARS-CoV-2. Nat Rev Microbiol 2023; 21:361-379. [PMID: 37020110 DOI: 10.1038/s41579-023-00878-2] [Citation(s) in RCA: 178] [Impact Index Per Article: 178.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2023] [Indexed: 04/07/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths and substantial morbidity worldwide. Intense scientific effort to understand the biology of SARS-CoV-2 has resulted in daunting numbers of genomic sequences. We witnessed evolutionary events that could mostly be inferred indirectly before, such as the emergence of variants with distinct phenotypes, for example transmissibility, severity and immune evasion. This Review explores the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events. We examine the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years, together with the implications of immune escape and reinfections, and the increasing evidence for and potential relevance of recombination. In order to understand how major lineages, such as variants of concern (VOCs), are generated, we contrast the evidence for the chronic infection model underlying the emergence of VOCs with the possibility of an animal reservoir playing a role in SARS-CoV-2 evolution, and conclude that the former is more likely. We evaluate uncertainties and outline scenarios for the possible future evolutionary trajectories of SARS-CoV-2.
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Affiliation(s)
- Peter V Markov
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
- London School of Hygiene & Tropical Medicine, University of London, London, UK.
| | - Mahan Ghafari
- Big Data Institute, University of Oxford, Oxford, UK
| | - Martin Beer
- Institute of Diagnostic Virology, Friedrich-Loeffler-Institut, Insel Riems, Germany
| | | | - Peter Simmonds
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolaos I Stilianakis
- European Commission, Joint Research Centre (JRC), Ispra, Italy
- Department of Biometry and Epidemiology, University of Erlangen-Nuremberg, Erlangen, Germany
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29
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Dellicour S, Hong SL, Hill V, Dimartino D, Marier C, Zappile P, Harkins GW, Lemey P, Baele G, Duerr R, Heguy A. Variant-specific introduction and dispersal dynamics of SARS-CoV-2 in New York City - from Alpha to Omicron. PLoS Pathog 2023; 19:e1011348. [PMID: 37071654 PMCID: PMC10180688 DOI: 10.1371/journal.ppat.1011348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/12/2023] [Accepted: 04/09/2023] [Indexed: 04/19/2023] Open
Abstract
Since the latter part of 2020, SARS-CoV-2 evolution has been characterised by the emergence of viral variants associated with distinct biological characteristics. While the main research focus has centred on the ability of new variants to increase in frequency and impact the effective reproductive number of the virus, less attention has been placed on their relative ability to establish transmission chains and to spread through a geographic area. Here, we describe a phylogeographic approach to estimate and compare the introduction and dispersal dynamics of the main SARS-CoV-2 variants - Alpha, Iota, Delta, and Omicron - that circulated in the New York City area between 2020 and 2022. Notably, our results indicate that Delta had a lower ability to establish sustained transmission chains in the NYC area and that Omicron (BA.1) was the variant fastest to disseminate across the study area. The analytical approach presented here complements non-spatially-explicit analytical approaches that seek a better understanding of the epidemiological differences that exist among successive SARS-CoV-2 variants of concern.
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Affiliation(s)
- Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Samuel L. Hong
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Verity Hill
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Dacia Dimartino
- Genome Technology Center, Office for Science and Research, NYU Langone Health, New York, New York, United States of America
| | - Christian Marier
- Genome Technology Center, Office for Science and Research, NYU Langone Health, New York, New York, United States of America
| | - Paul Zappile
- Genome Technology Center, Office for Science and Research, NYU Langone Health, New York, New York, United States of America
| | - Gordon W. Harkins
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Philippe Lemey
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Guy Baele
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Laboratory for Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium
| | - Ralf Duerr
- Department of Microbiology, NYU Grossman School of Medicine, New York, New York, United States of America
- Department of Medicine, NYU Grossman School of Medicine, New York, New York, United States of America
- Vaccine Center, NYU Grossman School of Medicine, New York, New York, United States of America
| | - Adriana Heguy
- Genome Technology Center, Office for Science and Research, NYU Langone Health, New York, New York, United States of America
- Department of Pathology, NYU Grossman School of Medicine, New York, New York, United States of America
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30
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Carabelli AM, Peacock TP, Thorne LG, Harvey WT, Hughes J, Peacock SJ, Barclay WS, de Silva TI, Towers GJ, Robertson DL. SARS-CoV-2 variant biology: immune escape, transmission and fitness. Nat Rev Microbiol 2023; 21:162-177. [PMID: 36653446 PMCID: PMC9847462 DOI: 10.1038/s41579-022-00841-7] [Citation(s) in RCA: 180] [Impact Index Per Article: 180.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 01/19/2023]
Abstract
In late 2020, after circulating for almost a year in the human population, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibited a major step change in its adaptation to humans. These highly mutated forms of SARS-CoV-2 had enhanced rates of transmission relative to previous variants and were termed 'variants of concern' (VOCs). Designated Alpha, Beta, Gamma, Delta and Omicron, the VOCs emerged independently from one another, and in turn each rapidly became dominant, regionally or globally, outcompeting previous variants. The success of each VOC relative to the previously dominant variant was enabled by altered intrinsic functional properties of the virus and, to various degrees, changes to virus antigenicity conferring the ability to evade a primed immune response. The increased virus fitness associated with VOCs is the result of a complex interplay of virus biology in the context of changing human immunity due to both vaccination and prior infection. In this Review, we summarize the literature on the relative transmissibility and antigenicity of SARS-CoV-2 variants, the role of mutations at the furin spike cleavage site and of non-spike proteins, the potential importance of recombination to virus success, and SARS-CoV-2 evolution in the context of T cells, innate immunity and population immunity. SARS-CoV-2 shows a complicated relationship among virus antigenicity, transmission and virulence, which has unpredictable implications for the future trajectory and disease burden of COVID-19.
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Affiliation(s)
| | - Thomas P Peacock
- Department of Infectious Disease, St Mary's Medical School, Imperial College London, London, UK
| | - Lucy G Thorne
- Division of Infection and Immunity, University College London, London, UK
| | - William T Harvey
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
- Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Addenbrookes Hospital, Cambridge, UK
| | - Wendy S Barclay
- Department of Infectious Disease, St Mary's Medical School, Imperial College London, London, UK
| | - Thushan I de Silva
- Department of Infection, Immunity and Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
| | - Greg J Towers
- Division of Infection and Immunity, University College London, London, UK
| | - David L Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow, UK.
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31
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Scheithauer S, Dilthey A, Bludau A, Ciesek S, Corman V, Donker T, Eckmanns T, Egelkamp R, Grundmann H, Häcker G, Kaase M, Lange B, Mellmann A, Mielke M, Pletz M, Salzberger B, Thürmer A, Widmer A, Wieler LH, Wolff T, Gatermann S, Semmler T. [Establishment of genomic pathogen surveillance to strengthen pandemic preparedness and infection prevention in Germany]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2023; 66:443-449. [PMID: 36811648 PMCID: PMC9945818 DOI: 10.1007/s00103-023-03680-w] [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] [Indexed: 02/24/2023]
Abstract
The SARS-CoV‑2 pandemic has shown a deficit of essential epidemiological infrastructure, especially with regard to genomic pathogen surveillance in Germany. In order to prepare for future pandemics, the authors consider it urgently necessary to remedy this existing deficit by establishing an efficient infrastructure for genomic pathogen surveillance. Such a network can build on structures, processes, and interactions that have already been initiated regionally and further optimize them. It will be able to respond to current and future challenges with a high degree of adaptability.The aim of this paper is to address the urgency and to outline proposed measures for establishing an efficient, adaptable, and responsive genomic pathogen surveillance network, taking into account external framework conditions and internal standards. The proposed measures are based on global and country-specific best practices and strategy papers. Specific next steps to achieve an integrated genomic pathogen surveillance include linking epidemiological data with pathogen genomic data; sharing and coordinating existing resources; making surveillance data available to relevant decision-makers, the public health service, and the scientific community; and engaging all stakeholders. The establishment of a genomic pathogen surveillance network is essential for the continuous, stable, active surveillance of the infection situation in Germany, both during pandemic phases and beyond.
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Affiliation(s)
- Simone Scheithauer
- Institut für Krankenhaushygiene und Infektiologie, Universitätsmedizin Göttingen (UMG), Georg-August Universität Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Deutschland.
| | - Alexander Dilthey
- Medizinische Mikrobiologie und Krankenhaushygiene, Universitätsklinikum Düsseldorf, Düsseldorf, Deutschland
| | - Anna Bludau
- Institut für Krankenhaushygiene und Infektiologie, Universitätsmedizin Göttingen (UMG), Georg-August Universität Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Deutschland
| | - Sandra Ciesek
- Institut für Medizinische Virologie, Universitätsklinikum Frankfurt, Frankfurt am Main, Deutschland
| | - Victor Corman
- Institut für Virologie, Charité Universitätsmedizin Berlin, Berlin, Deutschland
| | - Tjibbe Donker
- Institut für Infektionsprävention und Krankenhaushygiene, Universitätsklinikum Freiburg, Freiburg, Deutschland
| | | | - Richard Egelkamp
- Next Generation Sequencing, Niedersächsisches Landesgesundheitsamt, Hannover, Deutschland
| | - Hajo Grundmann
- Institut für Infektionsprävention und Krankenhaushygiene, Universitätsklinikum Freiburg, Freiburg, Deutschland
| | - Georg Häcker
- Institut für Medizinische Mikrobiologie und Hygiene, Universitätsklinikum Freiburg, Freiburg, Deutschland
| | - Martin Kaase
- Institut für Krankenhaushygiene und Infektiologie, Universitätsmedizin Göttingen (UMG), Georg-August Universität Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Deutschland
| | - Berit Lange
- Abteilung Epidemiologie, Helmholtz-Zentrum für Infektionsforschung, Braunschweig, Deutschland
| | - Alexander Mellmann
- Institut für Hygiene, Universitätsklinikum Münster, Münster, Deutschland
| | | | - Mathias Pletz
- Institut für Infektionsmedizin und Krankenhaushygiene, Universitätsklinikum Jena, Jena, Deutschland
| | - Bernd Salzberger
- Infektiologie, Abteilung für Krankenhaushygiene und Infektiologie, Universitätsklinikum Regensburg, Regensburg, Deutschland
| | | | - Andreas Widmer
- Abteilung für Infektiologie und Spitalhygiene, Universitätsspital Basel, Basel, Schweiz
| | | | | | - Sören Gatermann
- Institut für Hygiene und Mikrobiologie, Ruhr-Universität Bochum, Bochum, Deutschland
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32
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Perez-Zabaleta M, Archer A, Khatami K, Jafferali MH, Nandy P, Atasoy M, Birgersson M, Williams C, Cetecioglu Z. Long-term SARS-CoV-2 surveillance in the wastewater of Stockholm: What lessons can be learned from the Swedish perspective? THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:160023. [PMID: 36356735 PMCID: PMC9640212 DOI: 10.1016/j.scitotenv.2022.160023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/14/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Wastewater-based epidemiology (WBE) can be used to track the spread of SARS-CoV-2 in a population. This study presents the learning outcomes from over two-year long monitoring of SARS-CoV-2 in Stockholm, Sweden. The three main wastewater treatment plants in Stockholm, with a total of six inlets, were monitored from April 2020 until June 2022 (in total 600 samples). This spans five major SARS-CoV-2 waves, where WBE data provided early warning signals for each wave. Further, the measured SARS-CoV-2 content in the wastewater correlated significantly with the level of positive COVID-19 tests (r = 0.86; p << 0.0001) measured by widespread testing of the population. Moreover, as a proof-of-concept, six SARS-CoV-2 variants of concern were monitored using hpPCR assay, demonstrating that variants can be traced through wastewater monitoring. During this long-term surveillance, two sampling protocols, two RNA concentration/extraction methods, two calculation approaches, and normalization to the RNA virus Pepper mild mottle virus (PMMoV) were evaluated. In addition, a study of storage conditions was performed, demonstrating that the decay of viral RNA was significantly reduced upon the addition of glycerol to the wastewater before storage at -80 °C. Our results provide valuable information that can facilitate the incorporation of WBE as a prediction tool for possible future outbreaks of SARS-CoV-2 and preparations for future pandemics.
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Affiliation(s)
- Mariel Perez-Zabaleta
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Amena Archer
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Kasra Khatami
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Mohammed Hakim Jafferali
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Prachi Nandy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Merve Atasoy
- Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden
| | - Madeleine Birgersson
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Cecilia Williams
- Department of Protein Science, KTH Royal Institute of Technology, Science for Life Laboratory, Solna, Sweden
| | - Zeynep Cetecioglu
- Department of Industrial Biotechnology, KTH Royal Institute of Technology, AlbaNova University Center, SE-10691 Stockholm, Sweden; Department of Chemical Engineering, KTH Royal Institute of Technology, SE-10044, Sweden.
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33
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Andersen MS, Marsicano C, Pineda Torres M, Slusky D. Texas Senate Bill 8 significantly reduced travel to abortion clinics in Texas. Front Glob Womens Health 2023; 4:1117724. [PMID: 37020904 PMCID: PMC10067718 DOI: 10.3389/fgwh.2023.1117724] [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: 12/06/2022] [Accepted: 02/28/2023] [Indexed: 04/07/2023] Open
Abstract
The Dobbs v. Jackson decision by the United States Supreme Court has rescinded the constitutional guarantee of abortion across the United States. As a result, at least 13 states have banned abortion access with unknown effects. Using "Texas" SB8 law that similarly restricted abortions in Texas, we provide insight into how individuals respond to these restrictions using aggregated and anonymized human mobility data. We find that "Texas" SB 8 law reduced mobility near abortion clinics in Texas by people who live in Texas and those who live outside the state. We also find that mobility from Texas to abortion clinics in other states increased, with notable increases in Missouri and Arkansas, two states that subsequently enacted post-Dobbs bans. These results highlight the importance of out-of-state abortion services for women living in highly restrictive states.
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Affiliation(s)
- Martin S. Andersen
- Department of Economics, University of North Carolina at Greensboro, Greensboro, NC, United States
- Correspondence: Martin S. Andersen
| | | | - Mayra Pineda Torres
- School of Economics, Georgia Institute of Technology, Atlanta, GA, United States
| | - David Slusky
- Department of Economics, University of Kansas, Lawrence, KS, United States
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34
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Tegally H, Khan K, Huber C, de Oliveira T, Kraemer MUG. Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves. J Travel Med 2022; 29:taac134. [PMID: 36367200 PMCID: PMC9793401 DOI: 10.1093/jtm/taac134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 09/20/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Human mobility changed in unprecedented ways during the SARS-CoV-2 pandemic. In March and April 2020, when lockdowns and large travel restrictions began in most countries, global air-travel almost entirely halted (92% decrease in commercial global air travel in the months between February and April 2020). Initial recovery in global air travel started around July 2020 and subsequently nearly tripled between May and July 2021. Here, we aim to establish a preliminary link between global mobility patterns and the synchrony of SARS-CoV-2 epidemic waves across the world. METHODS We compare epidemic peaks and human global mobility in two time periods: November 2020 to February 2021 (when just over 70 million passengers travelled) and November 2021 to February 2022 (when more than 200 million passengers travelled). We calculate the time interval during which continental epidemic peaks occurred for both of these time periods, and we calculate the pairwise correlations of epidemic waves between all pairs of countries for the same time periods. RESULTS We find that as air travel increases at the end of 2021, epidemic peaks around the world are more synchronous with one another, both globally and regionally. Continental epidemic peaks occur globally within a 20 day interval at the end of 2021 compared with 73 days at the end of 2020, and epidemic waves globally are more correlated with one another at the end of 2021. CONCLUSIONS This suggests that the rebound in human mobility dictates the synchrony of global and regional epidemic waves. In line with theoretical work, we show that in a more connected world, epidemic dynamics are more synchronized.
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Affiliation(s)
- Houriiyah Tegally
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | - Tulio de Oliveira
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, United Kingdom
- Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
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35
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Prosperi M, Rife B, Marini S, Salemi M. Transmission cluster characteristics of global, regional, and lineage-specific SARS-CoV-2 phylogenies. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2940-2944. [PMID: 36780250 PMCID: PMC9912475 DOI: 10.1109/bibm55620.2022.9995364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
The SARS-CoV-2 pandemic has been presenting in periodic waves and multiple variants, of which some dominated over time with increased transmissibility. SARS-CoV-2 is still adapting in the human population, thus it is crucial to understand its evolutionary patterns and dynamics ahead of time. In this work, we analyzed transmission clusters and topology of SARS-CoV-2 phylogenies at the global, regional (North America) and clade-specific (Delta and Omicron) epidemic scales. We used the Nextstrain's nCov open global all-time phylogeny (September 2022, 2,698 strains, 2,243 for North America, 499 for Delta21A, and 543 for Omicron20M), with Nextstrain's clade annotation and Pango lineages. Transmission clusters were identified using Phylopart, DYNAMITE, and several tree imbalance measures were calculated, including staircase-ness, Sackin and Colless index. We found that the phylogenetic clustering profiles of the global epidemic have highest diversification at a distance threshold of 3% (divergence of 10, where the tree sampled median is 49). Phylopart and DYNAMITE clusters moderately-to-highly agree with the Pango nomenclature and the Nextstrain's clade. At the regional and clade-specific scale, transmission clustering profiles tend to flatten and similar clusters are found at distance thresholds between 0.05% and 25%. All the considered phylogenies exhibit high tree imbalance with respect to what expected in random phylogenies, suggesting short infection times and antigenic drift, perhaps due to progressive transition from innate to adaptive immunity in the population.
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Affiliation(s)
- Mattia Prosperi
- Department of Epidemiology, College of Public Health and
Health Professions, University of Florida Gainesville, Fl,
USA
| | - Brittany Rife
- Department of Pathology, Immunology and Laboratory
Medicine, College of Medicine, University of Florida
Gainesville, Fl, USA
| | - Simone Marini
- Department of Epidemiology, College of Public Health and
Health Professions, University of Florida Gainesville, Fl,
USA
| | - Marco Salemi
- Department of Pathology, Immunology and Laboratory
Medicine, College of Medicine, University of Florida
Gainesville, Fl, USA
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36
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Reichmuth ML, Hodcroft EB, Riou J, Neher RA, Hens N, Althaus CL. Impact of cross-border-associated cases on the SARS-CoV-2 epidemic in Switzerland during summer 2020 and 2021. Epidemics 2022; 41:100654. [PMID: 36444785 PMCID: PMC9671612 DOI: 10.1016/j.epidem.2022.100654] [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: 05/27/2022] [Revised: 10/01/2022] [Accepted: 11/12/2022] [Indexed: 11/18/2022] Open
Abstract
During the summers of 2020 and 2021, the number of confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Switzerland remained at relatively low levels, but grew steadily over time. It remains unclear to what extent epidemic growth during these periods was a result of the relaxation of local control measures or increased traveling and subsequent importation of cases. A better understanding of the role of cross-border-associated cases (imports) on the local epidemic dynamics will help to inform future surveillance strategies. We analyzed routine surveillance data of confirmed cases of SARS-CoV-2 in Switzerland from 1 June to 30 September 2020 and 2021. We used a stochastic branching process model that accounts for superspreading of SARS-CoV-2 to simulate epidemic trajectories in absence and in presence of imports during summer 2020 and 2021. The Swiss Federal Office of Public Health reported 22,919 and 145,840 confirmed cases of SARS-CoV-2 from 1 June to 30 September 2020 and 2021, respectively. Among cases with known place of exposure, 27% (3,276 of 12,088) and 25% (1,110 of 4,368) reported an exposure abroad in 2020 and 2021, respectively. Without considering the impact of imported cases, the steady growth of confirmed cases during summer periods would be consistent with a value of Re that is significantly above the critical threshold of 1. In contrast, we estimated Re at 0.84 (95% credible interval, CrI: 0.78-0.90) in 2020 and 0.82 (95% CrI: 0.74-0.90) in 2021 when imported cases were taken into account, indicating that the local Re was below the critical threshold of 1 during summer. In Switzerland, cross-border-associated SARS-CoV-2 cases had a considerable impact on the local transmission dynamics and can explain the steady growth of the epidemic during the summers of 2020 and 2021.
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Affiliation(s)
- Martina L. Reichmuth
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Correspondence to: Institute of Social and Preventive Medicine, University of Bern, Mittelstrasse 43, CH-3012 Bern, Switzerland
| | - Emma B. Hodcroft
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Julien Riou
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland,Federal Office of Public Health, Liebefeld, Switzerland
| | - Richard A. Neher
- Swiss Institute of Bioinformatics, Lausanne, Switzerland,Biozentrum, University of Basel, Basel, Switzerland
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium,Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christian L. Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
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37
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Tegally H, Wilkinson E, Martin D, Moir M, Brito A, Giovanetti M, Khan K, Huber C, Bogoch II, San JE, Tsui JLH, Poongavanan J, Xavier JS, Candido DDS, Romero F, Baxter C, Pybus OG, Lessells R, Faria NR, Kraemer MUG, de Oliveira T. Global Expansion of SARS-CoV-2 Variants of Concern: Dispersal Patterns and Influence of Air Travel. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.11.22.22282629. [PMID: 36451885 PMCID: PMC9709793 DOI: 10.1101/2022.11.22.22282629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In many regions of the world, the Alpha, Beta and Gamma SARS-CoV-2 Variants of Concern (VOCs) co-circulated during 2020-21 and fueled waves of infections. During 2021, these variants were almost completely displaced by the Delta variant, causing a third wave of infections worldwide. This phenomenon of global viral lineage displacement was observed again in late 2021, when the Omicron variant disseminated globally. In this study, we use phylogenetic and phylogeographic methods to reconstruct the dispersal patterns of SARS-CoV-2 VOCs worldwide. We find that the source-sink dynamics of SARS-CoV-2 varied substantially by VOC, and identify countries that acted as global hubs of variant dissemination, while other countries became regional contributors to the export of specific variants. We demonstrate a declining role of presumed origin countries of VOCs to their global dispersal: we estimate that India contributed <15% of all global exports of Delta to other countries and South Africa <1-2% of all global Omicron exports globally. We further estimate that >80 countries had received introductions of Omicron BA.1 100 days after its inferred date of emergence, compared to just over 25 countries for the Alpha variant. This increased speed of global dissemination was associated with a rebound in air travel volume prior to Omicron emergence in addition to the higher transmissibility of Omicron relative to Alpha. Our study highlights the importance of global and regional hubs in VOC dispersal, and the speed at which highly transmissible variants disseminate through these hubs, even before their detection and characterization through genomic surveillance. Highlights Global phylogenetic analysis reveals relationship between air travel and speed of dispersal of SARS-CoV-2 variants of concern (VOCs)Omicron VOC spread to 5x more countries within 100 days of its emergence compared to all other VOCsOnward transmission and dissemination of VOCs Delta and Omicron was primarily from secondary hubs rather than initial country of detection during a time of increased global air travelAnalysis highlights highly connected countries identified as major global and regional exporters of VOCs.
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Affiliation(s)
- Houriiyah Tegally
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Eduan Wilkinson
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Darren Martin
- Wellcome Centre for Infectious Diseases Research in Africa (CIDRI-Africa), Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Monika Moir
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Anderson Brito
- Instituto Todos pela Saúde, São Paulo, São Paulo, Brazil
| | - Marta Giovanetti
- Laboratorio de Flavivirus, Fundacao Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genética Celular e Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Science and Technology for Humans and the Environment, University of Campus Bio-Medico di Roma, Rome, Italy
| | - Kamran Khan
- BlueDot, Toronto, Canada
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | | | - Isaac I Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
| | - James Emmanuel San
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | | | - Jenicca Poongavanan
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Joicymara S Xavier
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Unaí, Brazil
| | - Darlan da S Candido
- MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Filipe Romero
- MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Cheryl Baxter
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Oliver G Pybus
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
| | - Richard Lessells
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Nuno R Faria
- MRC Centre for Global Infectious Disease Analysis and Department of Infectious Disease Epidemiology, Jameel Institute, School of Public Health, Imperial College London, London, UK
- Departamento de Moléstias Infecciosas e Parasitárias e Instituto de Medicina Tropical da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Biology, University of Oxford, Oxford, UK
| | - Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Tulio de Oliveira
- Centre for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
- KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP), Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
- Department of Global Health, University of Washington, Seattle, WA, USA
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38
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Murari TB, Fonseca LMDS, Pereira HBDB, Nascimento Filho AS, Saba H, Scorza FA, G. de Almeida AC, Maciel ELN, Mendes JFF, Rocha Filho TM, David JR, Badaró R, Machado BAS, Moret MA. Retrospective Cohort Study of COVID-19 in Patients of the Brazilian Public Health System with SARS-CoV-2 Omicron Variant Infection. Vaccines (Basel) 2022; 10:1504. [PMID: 36146584 PMCID: PMC9500832 DOI: 10.3390/vaccines10091504] [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: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/04/2022] [Indexed: 12/02/2022] Open
Abstract
Several vaccines against COVID-19 are now available, based on different techniques and made by different laboratories spread around the world. With the roll out of the vaccination process in an advanced stage in many countries, the reduced risk of hospitalization due to the Omicron variant relative to the Delta variant infection, despite the higher transmission risk of Omicron, may lead to a misinterpretation of the results, as infection by Omicron is associated with a significant reduction in severe outcomes and shorter hospitalization time than the Delta variant. We compared the in-hospital mortality due to the Omicron (Jan-Mar 2022) with Gamma (Jan 2021) and Delta (Oct-Dec 2021) variants of patients in the Brazilian public health system. This study also discusses the decrease in booster vaccine effectiveness in patients hospitalized due to the Omicron variant compared with the Delta variant. Without a remodeling of vaccines for new variants, booster doses may be necessary with a shorter time interval.
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Affiliation(s)
- Thiago B. Murari
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
| | - Larissa Moraes dos Santos Fonseca
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- SENAI Institute of Innovation (ISI) in Health Advanced Systems, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
| | - Hernane B. de B. Pereira
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, Salvador 41150-000, BA, Brazil
| | | | - Hugo Saba
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, Salvador 41150-000, BA, Brazil
| | - Fulvio A. Scorza
- Disciplina de Neurociência, Escola Paulista de Medicina, Universidade Federal de São Paulo (EPM/UNIFESP), São Paulo 04021-001, SP, Brazil
| | - Antônio-Carlos G. de Almeida
- Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), São João del-Rei 36307-352, MG, Brazil
| | - Ethel L. N. Maciel
- Laboratório de Epidemiologia, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, Brazil
| | - José F. F. Mendes
- Departamento de Física and I3N, Universidade de Aveiro, 3880 Aveiro, Portugal
| | - Tarcísio M. Rocha Filho
- International Center for Condensed Matter Physics and Instituto de Física, Universidade de Brasília, Brasília 70910-900, DF, Brazil
| | - John R. David
- Faculty of Public Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Immunology and Infectious Diseases, Harvard Medical School, Boston, MA 02115, USA
| | - Roberto Badaró
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- SENAI Institute of Innovation (ISI) in Health Advanced Systems, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
| | - Bruna Aparecida Souza Machado
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- SENAI Institute of Innovation (ISI) in Health Advanced Systems, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
| | - Marcelo A. Moret
- Modelagem Computacional, University Center SENAI/CIMATEC, Salvador 41650-010, BA, Brazil
- Departamento de Ciências Exatas e da Terra, Universidade do Estado da Bahia, Salvador 41150-000, BA, Brazil
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