1
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Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Bakhash SAM, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, Bedford T. Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county. PLoS Pathog 2024; 20:e1012117. [PMID: 38530853 PMCID: PMC10997136 DOI: 10.1371/journal.ppat.1012117] [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: 09/26/2023] [Revised: 04/05/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.
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Affiliation(s)
- Miguel I. Paredes
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Amanda C. Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Lauren Frisbie
- Washington State Department of Health, Shoreline, Washington, United States of America
| | - Louise H. Moncla
- The University of Pennsylvania, Department of Pathobiology, Philadelphia, Pennsylvania, United States of America
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Shah A. Mohamed Bakhash
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Kevin Kong
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Isabel Arnould
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Tien V. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Seffir T. Wendm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Pooneh Hajian
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Sean Ellis
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Patrick C. Mathias
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Alexander L. Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Lea M. Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Chris D. Frazar
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Erica Ryke
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Weizhi Zhong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Machiko Threlkeld
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Melissa Truong
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
| | - Hanna N. Oltean
- Washington State Department of Health, Shoreline, Washington, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Helen Chu
- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, United States of America
| | - Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
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2
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Seibel RL, Meadows AJ, Mundt C, Tildesley M. Modeling target-density-based cull strategies to contain foot-and-mouth disease outbreaks. PeerJ 2024; 12:e16998. [PMID: 38436010 PMCID: PMC10909358 DOI: 10.7717/peerj.16998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 02/02/2024] [Indexed: 03/05/2024] Open
Abstract
Total ring depopulation is sometimes used as a management strategy for emerging infectious diseases in livestock, which raises ethical concerns regarding the potential slaughter of large numbers of healthy animals. We evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom (UK), which may allow for some farms within rings around infected premises (IPs) to escape depopulation. We simulated this reduced farm density, or "target density", strategy using a spatially-explicit, stochastic, state-transition algorithm. We modeled FMD spread in four counties in the UK that have different farm demographics, using 740,000 simulations in a full-factorial analysis of epidemic impact measures (i.e., culled animals, culled farms, and epidemic length) and cull strategy parameters (i.e., target farm density, daily farm cull capacity, and cull radius). All of the cull strategy parameters listed above were drivers of epidemic impact. Our simulated target density strategy was usually more effective at combatting FMD compared with traditional total ring depopulation when considering mean culled animals and culled farms and was especially effective when daily farm cull capacity was low. The differences in epidemic impact measures among the counties are likely driven by farm demography, especially differences in cattle and farm density. To prevent over-culling and the associated economic, organizational, ethical, and psychological impacts, the target density strategy may be worth considering in decision-making processes for future control of FMD and other diseases.
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Affiliation(s)
- Rachel L. Seibel
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Amanda J. Meadows
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
- Ginkgo Bioworks, San Bruno, California, United States
| | - Christopher Mundt
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States
| | - Michael Tildesley
- Mathematics Institute, University of Warwick, Coventry, West Midlands, United Kingdom
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3
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Scotch M, Lauer K, Wieben ED, Cherukuri Y, Cunningham JM, Klee EW, Harrington JJ, Lau JS, McDonough SJ, Mutawe M, O'Horo JC, Rentmeester CE, Schlicher NR, White VT, Schneider SK, Vedell PT, Wang X, Yao JD, Pritt BS, Norgan AP. Genomic epidemiology reveals the dominance of Hennepin County in the transmission of SARS-CoV-2 in Minnesota from 2020 to 2022. mSphere 2023; 8:e0023223. [PMID: 37882516 PMCID: PMC10871168 DOI: 10.1128/msphere.00232-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/20/2023] [Indexed: 10/27/2023] Open
Abstract
IMPORTANCE We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission "sinks." In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.
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Affiliation(s)
- Matthew Scotch
- Research Affiliate, Mayo Clinic, Phoenix, Arizona, USA
- Biodesign Institute, Arizona State University, Tempe, Arizona, USA
- College of Health Solutions, Arizona State University, Phoenix, Arizona, USA
| | - Kimberly Lauer
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Julie M. Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric W. Klee
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | | | - Julie S. Lau
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | | | - Mark Mutawe
- Center for Individualized Medicine, Rochester, Minnesota, USA
| | - John C. O'Horo
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Chad E. Rentmeester
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Saint Mary’s University of Minnesota, Winona, Minnesota, USA
| | - Nicole R. Schlicher
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Valerie T. White
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan K. Schneider
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter T. Vedell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Xiong Wang
- Minnesota Department of Health, St. Paul, Minnesota, USA
| | - Joseph D. Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bobbi S. Pritt
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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4
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Minor NR, Ramuta MD, Stauss MR, Harwood OE, Brakefield SF, Alberts A, Vuyk WC, Bobholz MJ, Rosinski JR, Wolf S, Lund M, Mussa M, Beversdorf LJ, Aliota MT, O'Connor SL, O'Connor DH. Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings. Sci Rep 2023; 13:21398. [PMID: 38049453 PMCID: PMC10696062 DOI: 10.1038/s41598-023-48352-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 11/25/2023] [Indexed: 12/06/2023] Open
Abstract
Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater andair sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.
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Affiliation(s)
| | - Mitchell D Ramuta
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | | | - Olivia E Harwood
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Savannah F Brakefield
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Alexandra Alberts
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - William C Vuyk
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Max J Bobholz
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Jenna R Rosinski
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Sydney Wolf
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Madelyn Lund
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - Madison Mussa
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | | | - Matthew T Aliota
- Department of Veterinary and Biomedical Sciences, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Shelby L O'Connor
- Wisconsin National Primate Research Center, Madison, WI, USA
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA
| | - David H O'Connor
- Wisconsin National Primate Research Center, Madison, WI, USA.
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, 555 Science Drive, Madison, WI, 53711, USA.
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5
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Kramer AM, Thornlow B, Ye C, De Maio N, McBroome J, Hinrichs AS, Lanfear R, Turakhia Y, Corbett-Detig R. Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations. Syst Biol 2023; 72:1039-1051. [PMID: 37232476 PMCID: PMC10627557 DOI: 10.1093/sysbio/syad031] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 05/14/2023] [Accepted: 06/22/2023] [Indexed: 05/27/2023] Open
Abstract
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.
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Affiliation(s)
- Alexander M Kramer
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Bryan Thornlow
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Cheng Ye
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Jakob McBroome
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California San Diego, San Diego, CA 92093, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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6
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Minor NR, Ramuta MD, Stauss MR, Harwood OE, Brakefield SF, Alberts A, Vuyk WC, Bobholz MJ, Rosinski JR, Wolf S, Lund M, Mussa M, Beversdorf LJ, Aliota MT, O’Connor SL, O’Connor DH. Metagenomic sequencing detects human respiratory and enteric viruses in air samples collected from congregate settings. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.28.23290648. [PMID: 37398492 PMCID: PMC10312882 DOI: 10.1101/2023.05.28.23290648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Innovative methods for evaluating virus risk and spread, independent of test-seeking behavior, are needed to improve routine public health surveillance, outbreak response, and pandemic preparedness. Throughout the COVID-19 pandemic, environmental surveillance strategies, including wastewater and air sampling, have been used alongside widespread individual-based SARS-CoV-2 testing programs to provide population-level data. These environmental surveillance strategies have predominantly relied on pathogen-specific detection methods to monitor viruses through space and time. However, this provides a limited picture of the virome present in an environmental sample, leaving us blind to most circulating viruses. In this study, we explore whether pathogen-agnostic deep sequencing can expand the utility of air sampling to detect many human viruses. We show that sequence-independent single-primer amplification sequencing of nucleic acids from air samples can detect common and unexpected human respiratory and enteric viruses, including influenza virus type A and C, respiratory syncytial virus, human coronaviruses, rhinovirus, SARS-CoV-2, rotavirus, mamastrovirus, and astrovirus.
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Affiliation(s)
| | - Mitchell D. Ramuta
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Olivia E. Harwood
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Savannah F. Brakefield
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Alexandra Alberts
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - William C. Vuyk
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Max J. Bobholz
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Jenna R. Rosinski
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Sydney Wolf
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Madelyn Lund
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - Madison Mussa
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Matthew T. Aliota
- Department of Veterinary and Biomedical Sciences, University of Minnesota, Twin Cities, Minneapolis, MN, USA
| | - Shelby L. O’Connor
- Wisconsin National Primate Research Center, Madison, WI USA
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
| | - David H. O’Connor
- Wisconsin National Primate Research Center, Madison, WI USA
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI, USA
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7
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Ramaiah A, Khubbar M, Akinyemi K, Bauer A, Carranza F, Weiner J, Bhattacharyya S, Payne D, Balakrishnan N. Genomic Surveillance Reveals the Rapid Expansion of the XBB Lineage among Circulating SARS-CoV-2 Omicron Lineages in Southeastern Wisconsin, USA. Viruses 2023; 15:1940. [PMID: 37766346 PMCID: PMC10535685 DOI: 10.3390/v15091940] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
SARS-CoV-2 caused a life-threatening COVID-19 pandemic outbreak worldwide. The Southeastern Region of Wisconsin, USA (SERW) includes large urban Milwaukee and six suburban counties, namely Kenosha, Ozaukee, Racine, Walworth, Washington and Waukesha. Due to the lack of detailed SARS-CoV-2 genomic surveillance in the suburban populations of the SERW, whole-genome sequencing was employed to investigate circulating SARS-CoV-2 lineages and characterize dominant XBB lineages among this SERW population from November 2021 to April 2023. For an unbiased data analysis, we combined our 6709 SARS-CoV-2 sequences with 1520 sequences from the same geographical region submitted by other laboratories. Our study shows that SARS-CoV-2 genomes were distributed into 357 lineages/sublineages belonging to 13 clades, of which 88.8% were from Omicron. We document dominant sublineages XBB.1.5 and surging XBB.1.16 and XBB.1.9.1 with a few additional functional mutations in Spike, which are known to contribute to higher viral reproduction, enhanced transmission and immune evasion. Mutational profile assessment of XBB.1.5 Spike identifies 38 defining mutations with high prevalence occurring in 49.8-99.6% of the sequences studied, of which 32 mutations were in three functional domains. Phylogenetic and genetic relatedness between XBB.1.5 sequences reveal potential virus transmission occurring within households and within and between Southeastern Wisconsin counties. A comprehensive phylogeny of XBB.1.5 with global sub-dataset sequences confirms the wide spread of genetically similar SARS-CoV-2 strains within the same geographical area. Altogether, this study identified proportions of circulating Omicron variants and genetic characterization of XBB.1.5 in the SERW population, which helped state and national public health agencies to make compelling mitigation efforts to reduce COVID-19 transmission in the communities and monitor emerging lineages for their impact on diagnostics, treatments and vaccines.
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Affiliation(s)
| | - Manjeet Khubbar
- City of Milwaukee Health Department, Milwaukee, WI 53202, USA
| | | | - Amy Bauer
- City of Milwaukee Health Department, Milwaukee, WI 53202, USA
| | | | - Joshua Weiner
- City of Milwaukee Health Department, Milwaukee, WI 53202, USA
| | | | - David Payne
- City of Milwaukee Health Department, Milwaukee, WI 53202, USA
| | - Nandhakumar Balakrishnan
- City of Milwaukee Health Department, Milwaukee, WI 53202, USA
- Georgia Public Health Laboratory, Decatur, GA 30033, USA
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8
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Scotch M, Lauer K, Wieben ED, Cherukuri Y, Cunningham JM, Klee EW, Harrington JJ, Lau JS, McDonough SJ, Mutawe M, O’Horo JC, Rentmeester CE, Schlicher NR, White VT, Schneider SK, Vedell PT, Wang X, Yao JD, Pritt BS, Norgan AP. Genomic epidemiology reveals the dominance of Hennepin County in transmission of SARS-CoV-2 in Minnesota from 2020-2022. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2022.07.24.22277978. [PMID: 35923324 PMCID: PMC9347287 DOI: 10.1101/2022.07.24.22277978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread, and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020-2022 and use Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest introduction into Minnesota was to Hennepin County from a domestic source around January 22, 2020; six weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota, and eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International (MSP) airport) totaling 45 (out of 107) over the two-year period. Southern Minnesota counties were most common for domestic introductions with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n=772) over the two-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases or major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have impact on virus diversity across each individual county.
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Affiliation(s)
- Matthew Scotch
- Research Affiliate, Mayo Clinic Arizona, Phoenix, AZ USA
- Biodesign Center for Environmental Health Engineering, Arizona State University, Tempe, AZ USA
- College of Health Solutions, Arizona State University, Phoenix, Arizona USA
| | - Kimberly Lauer
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
| | - Eric D. Wieben
- Department of Biochemistry and Molecular Biology, Mayo Clinic Rochester, Rochester, MN, USA
| | | | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Eric W Klee
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
- Center for Individualized Medicine, Rochester, MN, USA
| | | | - Julie S Lau
- Center for Individualized Medicine, Rochester, MN, USA
| | | | - Mark Mutawe
- Center for Individualized Medicine, Rochester, MN, USA
| | - John C. O’Horo
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Chad E. Rentmeester
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Saint Mary’s University of Minnesota, Winona, MN, USA
| | - Nicole R Schlicher
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Valerie T White
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan K Schneider
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter T Vedell
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, MN, USA
| | - Xiong Wang
- Minnesota Department of Health, St. Paul, MN, USA
| | - Joseph D Yao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Bobbi S Pritt
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew P Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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9
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Ren J, Liu M, Liu Y, Liu J. TransCode: Uncovering COVID-19 transmission patterns via deep learning. Infect Dis Poverty 2023; 12:14. [PMID: 36855184 PMCID: PMC9971690 DOI: 10.1186/s40249-023-01052-9] [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: 09/14/2022] [Accepted: 01/03/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning. METHODS We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available. RESULTS The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level. CONCLUSIONS The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.
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Affiliation(s)
- Jinfu Ren
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Mutong Liu
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Yang Liu
- grid.221309.b0000 0004 1764 5980Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.
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10
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Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Mohamed Bakhash SA, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, Bedford T. Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.12.15.22283536. [PMID: 36561171 PMCID: PMC9774227 DOI: 10.1101/2022.12.15.22283536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.
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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, Washington, USA
| | - Amanda C. Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Lauren Frisbie
- Washington State Department of Health, Shoreline, WA USA
| | - Louise H. Moncla
- The University of Pennsylvania, Department of Pathobiology, Philadelphia, PA
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - Kevin Kong
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Isabel Arnould
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Tien V. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Seffir T. Wendm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Pooneh Hajian
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Sean Ellis
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Patrick C. Mathias
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Alexander L. Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Lea M. Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Chris D. Frazar
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Erica Ryke
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Weizhi Zhong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Machiko Threlkeld
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Melissa Truong
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Helen Chu
- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA
| | - Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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11
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Banholzer N, Lison A, Özcelik D, Stadler T, Feuerriegel S, Vach W. The methodologies to assess the effectiveness of non-pharmaceutical interventions during COVID-19: a systematic review. Eur J Epidemiol 2022; 37:1003-1024. [PMID: 36152133 PMCID: PMC9510554 DOI: 10.1007/s10654-022-00908-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/15/2022] [Indexed: 11/26/2022]
Abstract
Non-pharmaceutical interventions, such as school closures and stay-at-home orders, have been implemented around the world to control the spread of SARS-CoV-2. Their effectiveness in improving health-related outcomes has been the subject of numerous empirical studies. However, these studies show fairly large variation among methodologies in use, reflecting the absence of an established methodological framework. On the one hand, variation in methodologies may be desirable to assess the robustness of results; on the other hand, a lack of common standards can impede comparability among studies. To establish a comprehensive overview over the methodologies in use, we conducted a systematic review of studies assessing the effectiveness of non-pharmaceutical interventions between January 1, 2020 and January 12, 2021 (n = 248). We identified substantial variation in methodologies with respect to study setting, outcome, intervention, methodological approach, and effectiveness assessment. On this basis, we point to shortcomings of existing studies and make recommendations for the design of future studies.
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Affiliation(s)
- Nicolas Banholzer
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Adrian Lison
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
| | - Dennis Özcelik
- Chemistry | Biology | Pharmacy Information Center, ETH Zurich, Zurich, Switzerland
| | - Tanja Stadler
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- LMU Munich School of Management, LMU Munich, Munich, Germany
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland
- Department of Environmental Sciences, University of Basel, Basel, Switzerland
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12
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Attwood SW, Hill SC, Aanensen DM, Connor TR, Pybus OG. Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic. Nat Rev Genet 2022; 23:547-562. [PMID: 35459859 PMCID: PMC9028907 DOI: 10.1038/s41576-022-00483-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/23/2022] [Indexed: 01/05/2023]
Abstract
Determining the transmissibility, prevalence and patterns of movement of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections is central to our understanding of the impact of the pandemic and to the design of effective control strategies. Phylogenies (evolutionary trees) have provided key insights into the international spread of SARS-CoV-2 and enabled investigation of individual outbreaks and transmission chains in specific settings. Phylodynamic approaches combine evolutionary, demographic and epidemiological concepts and have helped track virus genetic changes, identify emerging variants and inform public health strategy. Here, we review and synthesize studies that illustrate how phylogenetic and phylodynamic techniques were applied during the first year of the pandemic, and summarize their contributions to our understanding of SARS-CoV-2 transmission and control.
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Affiliation(s)
- Stephen W Attwood
- Department of Zoology, University of Oxford, Oxford, UK.
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK.
| | - Sarah C Hill
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK
| | - David M Aanensen
- Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas R Connor
- Pathogen Genomics Unit, Public Health Wales NHS Trust, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK.
- Department of Pathobiology and Population Sciences, Royal Veterinary College, University of London, London, UK.
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13
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Ari E, Vásárhelyi BM, Kemenesi G, Tóth GE, Zana B, Somogyi B, Lanszki Z, Röst G, Jakab F, Papp B, Kintses B. A Single Early Introduction Governed Viral Diversity in the Second Wave of SARS-CoV-2 Epidemic in Hungary. Virus Evol 2022; 8:veac069. [PMID: 35996591 PMCID: PMC9384595 DOI: 10.1093/ve/veac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/28/2022] [Accepted: 07/26/2022] [Indexed: 11/30/2022] Open
Abstract
Retrospective evaluation of past waves of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic is key for designing optimal interventions against future waves and novel pandemics. Here, we report on analysing genome sequences of SARS-CoV-2 from the first two waves of the epidemic in 2020 in Hungary, mirroring a suppression and a mitigation strategy, respectively. Our analysis reveals that the two waves markedly differed in viral diversity and transmission patterns. Specifically, unlike in several European areas or in the USA, we have found no evidence for early introduction and cryptic transmission of the virus in the first wave of the pandemic in Hungary. Despite the introduction of multiple viral lineages, extensive community spread was prevented by a timely national lockdown in March 2020. In sharp contrast, the majority of the cases in the much larger second wave can be linked to a single transmission lineage of the pan-European B.1.160 variant. This lineage was introduced unexpectedly early, followed by a 2-month-long cryptic transmission before a soar of detected cases in September 2020. Epidemic analysis has revealed that the dominance of this lineage in the second wave was not associated with an intrinsic transmission advantage. This finding is further supported by the rapid replacement of B.1.160 by the alpha variant (B.1.1.7) that launched the third wave of the epidemic in February 2021. Overall, these results illustrate how the founder effect in combination with the cryptic transmission, instead of repeated international introductions or higher transmissibility, can govern viral diversity.
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Affiliation(s)
- Eszter Ari
- HCEMM-BRC Metabolic Systems Biology Research Group , Temesvári krt. 62, 6726, Szeged, Hungary
- Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
- Department of Genetics, ELTE Eötvös Loránd University , Pázmány Péter sétány 1/C 1117, Budapest, Hungary
| | - Bálint Márk Vásárhelyi
- Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
- National Laboratory of Biotechnology, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
| | - Gábor Kemenesi
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Gábor Endre Tóth
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Brigitta Zana
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Balázs Somogyi
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Zsófia Lanszki
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Gergely Röst
- National Laboratory for Health Security, Bolyai Institute, University of Szeged , Aradi vértanúk tere 1, 6720 Szeged, Hungary
| | - Ferenc Jakab
- National Laboratory of Virology, Virological Research Group, Szentágothai Research Centre, University of Pécs , Ifjúság útja 20, 7624, Pécs, Hungary
- Faculty of Sciences, Institute of Biology, University of Pécs , Ifjúság útja 6, 7624, Pécs, Hungary
| | - Balázs Papp
- HCEMM-BRC Metabolic Systems Biology Research Group , Temesvári krt. 62, 6726, Szeged, Hungary
- Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
- National Laboratory of Biotechnology, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
| | - Bálint Kintses
- HCEMM-BRC Translational Microbiology Research Group , Temesvári krt. 62, 6726, Szeged, Hungary
- Synthetic and System Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
- National Laboratory of Biotechnology, Biological Research Centre, Eötvös Loránd Research Network (ELKH) , Temesvári krt. 62, 6726, Szeged, Hungary
- Department of Biochemistry and Molecular Biology, Faculty of Science and Informatics, University of Szeged , Közép fasor 52, 6726, Szeged, Hungary
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14
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Thornlow B, Kramer A, Ye C, De Maio N, McBroome J, Hinrichs AS, Lanfear R, Turakhia Y, Corbett-Detig R. Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Approaches. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2021.12.02.471004. [PMID: 35611334 PMCID: PMC9128781 DOI: 10.1101/2021.12.02.471004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mould. There are currently over 10 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) methods are more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, and ML and MP frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimizations produce more accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo , we therefore propose that, in the context of comprehensive genomic epidemiology of SARS-CoV-2, MP online phylogenetics approaches should be favored.
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Affiliation(s)
- Bryan Thornlow
- Department of Biomolecular Engineering, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Alexander Kramer
- Department of Biomolecular Engineering, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Cheng Ye
- Department of Electrical and Computer Engineering, University of California, San Diego; San Diego, CA 92093, USA
| | - Nicola De Maio
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus; Cambridge CB10 1SD, UK
| | - Jakob McBroome
- Department of Biomolecular Engineering, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Angie S. Hinrichs
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
| | - Robert Lanfear
- Department of Ecology and Evolution, Research School of Biology, Australian National University; Canberra, ACT 2601, Australia
| | - Yatish Turakhia
- Department of Electrical and Computer Engineering, University of California, San Diego; San Diego, CA 92093, USA
| | - Russell Corbett-Detig
- Department of Biomolecular Engineering, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
- Genomics Institute, University of California, Santa Cruz; Santa Cruz, CA 95064, USA
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15
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Carcereny A, Garcia-Pedemonte D, Martínez-Velázquez A, Quer J, Garcia-Cehic D, Gregori J, Antón A, Andrés C, Pumarola T, Chacón-Villanueva C, Borrego CM, Bosch A, Guix S, Pintó RM. Dynamics of SARS-CoV-2 Alpha (B.1.1.7) variant spread: The wastewater surveillance approach. ENVIRONMENTAL RESEARCH 2022; 208:112720. [PMID: 35074352 PMCID: PMC8782736 DOI: 10.1016/j.envres.2022.112720] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/02/2022] [Accepted: 01/08/2022] [Indexed: 05/25/2023]
Abstract
Wastewater based epidemiology (WBE) offers an overview of the SARS-CoV-2 variants circulating among the population thereby serving as a proper surveillance method. The variant of concern (VOC) Alpha was first identified in September 2020 in the United Kingdom, and rapidly became dominant across Europe. Our objective was to elucidate the Alpha VOC outcompetition rate and identify mutations in the spike glycoprotein (S) gene, indicative of the circulation of the Alpha VOC and/or other variants in the population through wastewater analysis. In the period covered by this study (November 2020-April 2021), forteen wastewater treatment plants (WWTPs) were weekly sampled. The total number of SARS-CoV-2 genome copies per L (GC/L) was determined with a Real-Time qPCR, targeting the N gene. Surveillance of the Alpha VOC circulation was ascertained using a duplex RT-qPCR, targeting and discriminating the S gene. Our results showed that in a period of 6 weeks the Alpha VOC was present in all the studied WWTPs, and became dominant in 11 weeks on average. The outcompetition rates of the Alpha VOC were estimated, and their relationship with different parameters statistically analyzed. The rapid spread of the Alpha VOC was influenced by its initial input and by the previous circulation of SARS-COV-2 in the population. This latter point could be explained by its higher transmissibility, particularly advantadgeous when a certain degree of herd immunity exists. Moreover, the presence of signature mutations of SARS-COV-2 variants were established by deep-sequencing of the complete S gene. The circulation of the Alpha VOC in the area under study was confirmed, and additionally two combinations of mutations in the S glycoprotein (T73A and D253N, and S477N and A522S) that could affect antibody binding were identified.
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Affiliation(s)
- Albert Carcereny
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain
| | - David Garcia-Pedemonte
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain
| | - Adán Martínez-Velázquez
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain
| | - Josep Quer
- Liver Unit, Liver Diseases - Viral Hepatitis, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Damir Garcia-Cehic
- Liver Unit, Liver Diseases - Viral Hepatitis, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Josep Gregori
- Liver Unit, Liver Diseases - Viral Hepatitis, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Andrés Antón
- Microbiology Department, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain
| | - Cristina Andrés
- Microbiology Department, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain
| | - Tomàs Pumarola
- Microbiology Department, Vall D'Hebron Institut de Recerca (VHIR), Vall D'Hebron Hospital Campus, Barcelona, Spain
| | | | - Carles M Borrego
- Catalan Institute for Water Research (ICRA), Girona, Spain; Group of Molecular Microbial Ecology, Institute of Aquatic Ecology, University of Girona, Spain
| | - Albert Bosch
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain.
| | - Susana Guix
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain.
| | - Rosa M Pintó
- Enteric Virus Laboratory, Section of Microbiology, Virology and Biotechnology, Department of Genetics, Microbiology and Statistics, School of Biology, University of Barcelona Diagonal 643, 08028, Barcelona, Spain; Enteric Virus Laboratory, Institute of Nutrition and Food Safety (INSA), University of Barcelona, Spain.
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16
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Cappello L, Kim J, Liu S, Palacios JA. Statistical Challenges in Tracking the Evolution of SARS-CoV-2. Stat Sci 2022; 37:162-182. [PMID: 36034090 PMCID: PMC9409356 DOI: 10.1214/22-sts853] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
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Affiliation(s)
- Lorenzo Cappello
- Lorenzo Cappello is Assistant Professor, Departments of Economics and Business, Universitat Pompeu Fabra, 08005, Spain
| | - Jaehee Kim
- Jaehee Kim is Assistant Professor, Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
| | - Sifan Liu
- Sifan Liu is a Ph.D. student, Department of Statistics, Stanford University, Stanford, California 94305, USA
| | - Julia A. Palacios
- Julia A. Palacios is Assistant Professor, Departments of Statistics and Biomedical Data Sciences, Stanford University, Stanford, California 94305, USA
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17
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Ledebur K, Kaleta M, Chen J, Lindner SD, Matzhold C, Weidle F, Wittmann C, Habimana K, Kerschbaumer L, Stumpfl S, Heiler G, Bicher M, Popper N, Bachner F, Klimek P. Meteorological factors and non-pharmaceutical interventions explain local differences in the spread of SARS-CoV-2 in Austria. PLoS Comput Biol 2022; 18:e1009973. [PMID: 35377873 PMCID: PMC9009775 DOI: 10.1371/journal.pcbi.1009973] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 04/14/2022] [Accepted: 02/28/2022] [Indexed: 12/23/2022] Open
Abstract
The drivers behind regional differences of SARS-CoV-2 spread on finer spatio-temporal scales are yet to be fully understood. Here we develop a data-driven modelling approach based on an age-structured compartmental model that compares 116 Austrian regions to a suitably chosen control set of regions to explain variations in local transmission rates through a combination of meteorological factors, non-pharmaceutical interventions and mobility. We find that more than 60% of the observed regional variations can be explained by these factors. Decreasing temperature and humidity, increasing cloudiness, precipitation and the absence of mitigation measures for public events are the strongest drivers for increased virus transmission, leading in combination to a doubling of the transmission rates compared to regions with more favourable weather. We conjecture that regions with little mitigation measures for large events that experience shifts toward unfavourable weather conditions are particularly predisposed as nucleation points for the next seasonal SARS-CoV-2 waves. How weather modulates the spread of SARS-CoV-2 on fine spatio-temporal scales is still not fully understood. Here we use a controlled regional comparison to isolate the impact of five different meteorological factors, four types of non-pharmaceutical interventions as well as individual-level mobility on transmission rates in Austria. We find that more than 60% of regional variations can be explained by these factors. Temperature and humidity relate inversely with transmission rates whereas cloudiness and precipitation correlate with increasing transmission. We also observe a particularly strong impact of restrictions targeting large events. Our results suggest that a combination of weather shifts towards winter conditions combined with little mitigation measures for large gatherings drive the early growth of seasonal SARS-CoV-2 waves.
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Affiliation(s)
- Katharina Ledebur
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Michaela Kaleta
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Jiaying Chen
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Simon D. Lindner
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Caspar Matzhold
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
| | - Florian Weidle
- Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria
| | | | | | | | - Sophie Stumpfl
- Austrian National Public Health Institute, Vienna, Austria
| | - Georg Heiler
- Complexity Science Hub Vienna, Vienna, Austria
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Martin Bicher
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
- dwh simulation services, dwh GmbH, Vienna, Austria
| | - Nikolas Popper
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
- dwh simulation services, dwh GmbH, Vienna, Austria
- Association for Decision Support Policy and Planning, DEXHELPP, Vienna, Austria
| | | | - Peter Klimek
- Medical University of Vienna, Section for Science of Complex Systems, CeMSIIS, Vienna, Austria
- Complexity Science Hub Vienna, Vienna, Austria
- * E-mail:
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18
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Mostefai F, Gamache I, N'Guessan A, Pelletier J, Huang J, Murall CL, Pesaranghader A, Gaonac'h-Lovejoy V, Hamelin DJ, Poujol R, Grenier JC, Smith M, Caron E, Craig M, Wolf G, Krishnaswamy S, Shapiro BJ, Hussin JG. Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages. Front Med (Lausanne) 2022; 9:826746. [PMID: 35265640 PMCID: PMC8899026 DOI: 10.3389/fmed.2022.826746] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.
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Affiliation(s)
- Fatima Mostefai
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
| | - Isabel Gamache
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
| | - Arnaud N'Guessan
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Justin Pelletier
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, United States
| | - Carmen Lia Murall
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | | | - Vanda Gaonac'h-Lovejoy
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
- Research Centre, CHU Sainte-Justine, Montreal, QC, Canada
| | - David J. Hamelin
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
- Research Centre, CHU Sainte-Justine, Montreal, QC, Canada
| | - Raphaël Poujol
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
| | | | - Martin Smith
- Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada
- Research Centre, CHU Sainte-Justine, Montreal, QC, Canada
| | - Etienne Caron
- Research Centre, CHU Sainte-Justine, Montreal, QC, Canada
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montreal, QC, Canada
| | - Morgan Craig
- Research Centre, CHU Sainte-Justine, Montreal, QC, Canada
- Département de Mathématiques et Statistique, Université de Montréal, Montreal, QC, Canada
| | - Guy Wolf
- Mila – Quebec AI institute, Montreal, QC, Canada
- Département de Mathématiques et Statistique, Université de Montréal, Montreal, QC, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, United States
- Department of Genetics, Yale University, New Haven, CT, United States
| | - B. Jesse Shapiro
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Julie G. Hussin
- Research Centre, Montreal Heart Institute, Montreal, QC, Canada
- Département de Médecine, Université de Montréal, Montreal, QC, Canada
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19
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Babiker A, Martin MA, Marvil C, Bellman S, Petit III RA, Bradley HL, Stittleburg VD, Ingersoll J, Kraft CS, Li Y, Zhang J, Paden CR, Read TD, Waggoner JJ, Koelle K, Piantadosi A. Unrecognized introductions of SARS-CoV-2 into the US state of Georgia shaped the early epidemic. Virus Evol 2022; 8:veac011. [PMID: 35317348 PMCID: PMC8933693 DOI: 10.1093/ve/veac011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/15/2022] [Accepted: 02/14/2022] [Indexed: 11/24/2022] Open
Abstract
In early 2020, as diagnostic and surveillance responses for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ramped up, attention focused primarily on returning international travelers. Here, we build on existing studies characterizing early patterns of SARS-CoV-2 spread within the USA by analyzing detailed clinical, molecular, and viral genomic data from the state of Georgia through March 2020. We find evidence for multiple early introductions into Georgia, despite relatively sparse sampling. Most sampled sequences likely stemmed from a single or small number of introductions from Asia three weeks prior to the state's first detected infection. Our analysis of sequences from domestic travelers demonstrates widespread circulation of closely related viruses in multiple US states by the end of March 2020. Our findings indicate that the exclusive focus on identifying SARS-CoV-2 in returning international travelers early in the pandemic may have led to a failure to recognize locally circulating infections for several weeks and point toward a critical need for implementing rapid, broadly targeted surveillance efforts for future pandemics.
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Affiliation(s)
- Ahmed Babiker
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Michael A Martin
- Department of Biology, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
- Population Biology, Ecology, and Evolution Graduate Program, Laney Graduate School, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Charles Marvil
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Stephanie Bellman
- Environmental Health Sciences PhD Program, Laney Graduate School, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Robert A Petit III
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Heath L Bradley
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Victoria D Stittleburg
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Jessica Ingersoll
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Colleen S Kraft
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Yan Li
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Jing Zhang
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Clinton R Paden
- Division of Viral Diseases, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA 30333, USA
| | - Timothy D Read
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Jesse J Waggoner
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Katia Koelle
- Department of Biology, Emory University, 201 Dowman Drive, Atlanta, GA 30322, USA
| | - Anne Piantadosi
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 201 Dowman Drive, Atlanta, GA 30322, USA
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20
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Pathak AK, Mishra GP, Uppili B, Walia S, Fatihi S, Abbas T, Banu S, Ghosh A, Kanampalliwar A, Jha A, Fatma S, Aggarwal S, Dhar MS, Marwal R, Radhakrishnan VS, Ponnusamy K, Kabra S, Rakshit P, Bhoyar RC, Jain A, Divakar MK, Imran M, Faruq M, Sowpati DT, Thukral L, Raghav SK, Mukerji M. Spatio-temporal dynamics of intra-host variability in SARS-CoV-2 genomes. Nucleic Acids Res 2022; 50:1551-1561. [PMID: 35048970 PMCID: PMC8860616 DOI: 10.1093/nar/gkab1297] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/09/2021] [Accepted: 01/13/2022] [Indexed: 12/13/2022] Open
Abstract
During the course of the COVID-19 pandemic, large-scale genome sequencing of SARS-CoV-2 has been useful in tracking its spread and in identifying variants of concern (VOC). Viral and host factors could contribute to variability within a host that can be captured in next-generation sequencing reads as intra-host single nucleotide variations (iSNVs). Analysing 1347 samples collected till June 2020, we recorded 16 410 iSNV sites throughout the SARS-CoV-2 genome. We found ∼42% of the iSNV sites to be reported as SNVs by 30 September 2020 in consensus sequences submitted to GISAID, which increased to ∼80% by 30th June 2021. Following this, analysis of another set of 1774 samples sequenced in India between November 2020 and May 2021 revealed that majority of the Delta (B.1.617.2) and Kappa (B.1.617.1) lineage-defining variations appeared as iSNVs before getting fixed in the population. Besides, mutations in RdRp as well as RNA-editing by APOBEC and ADAR deaminases seem to contribute to the differential prevalence of iSNVs in hosts. We also observe hyper-variability at functionally critical residues in Spike protein that could alter the antigenicity and may contribute to immune escape. Thus, tracking and functional annotation of iSNVs in ongoing genome surveillance programs could be important for early identification of potential variants of concern and actionable interventions.
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Affiliation(s)
- Ankit K Pathak
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | | | - Bharathram Uppili
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Safal Walia
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Saman Fatihi
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Tahseen Abbas
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Sofia Banu
- CSIR - Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, Telangana, India
| | - Arup Ghosh
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | | | - Atimukta Jha
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Sana Fatma
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Shifu Aggarwal
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Mahesh Shanker Dhar
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Robin Marwal
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | | | - Kalaiarasan Ponnusamy
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Sandhya Kabra
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Partha Rakshit
- Biotechnology Division, National Centre for Disease Control (NCDC), New Delhi, India
| | - Rahul C Bhoyar
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Abhinav Jain
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohit Kumar Divakar
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohamed Imran
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mohammed Faruq
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Divya Tej Sowpati
- CSIR - Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, Telangana, India
| | - Lipi Thukral
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sunil K Raghav
- Institute of Life Sciences (ILS), Bhubaneswar, Odisha, India
| | - Mitali Mukerji
- CSIR - Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India.,Indian Institute of Technology (IIT), Jodhpur, India
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21
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Li J, Lai S, Gao GF, Shi W. The emergence, genomic diversity and global spread of SARS-CoV-2. Nature 2021; 600:408-418. [PMID: 34880490 DOI: 10.1038/s41586-021-04188-6] [Citation(s) in RCA: 210] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 10/26/2021] [Indexed: 12/11/2022]
Abstract
Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.
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Affiliation(s)
- Juan Li
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - George F Gao
- National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.,CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology,, Chinese Academy of Sciences, Beijing, China.,Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing, China
| | - Weifeng Shi
- School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China. .,Key Laboratory of Etiology and Epidemiology of Emerging Infectious Diseases in the Universities of Shandong, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, China.
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22
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Terrier O, Si-Tahar M, Ducatez M, Chevalier C, Pizzorno A, Le Goffic R, Crépin T, Simon G, Naffakh N. Influenza viruses and coronaviruses: Knowns, unknowns, and common research challenges. PLoS Pathog 2021; 17:e1010106. [PMID: 34969061 PMCID: PMC8718010 DOI: 10.1371/journal.ppat.1010106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The development of safe and effective vaccines in a record time after the emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a remarkable achievement, partly based on the experience gained from multiple viral outbreaks in the past decades. However, the Coronavirus Disease 2019 (COVID-19) crisis also revealed weaknesses in the global pandemic response and large gaps that remain in our knowledge of the biology of coronaviruses (CoVs) and influenza viruses, the 2 major respiratory viruses with pandemic potential. Here, we review current knowns and unknowns of influenza viruses and CoVs, and we highlight common research challenges they pose in 3 areas: the mechanisms of viral emergence and adaptation to humans, the physiological and molecular determinants of disease severity, and the development of control strategies. We outline multidisciplinary approaches and technological innovations that need to be harnessed in order to improve preparedeness to the next pandemic.
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Affiliation(s)
- Olivier Terrier
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- CIRI, Centre International de Recherche en Infectiologie (Team VirPath), Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France
| | - Mustapha Si-Tahar
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- Inserm U1100, Research Center for Respiratory Diseases (CEPR), Université de Tours, Tours, France
| | - Mariette Ducatez
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- IHAP, UMR1225, Université de Toulouse, ENVT, INRAE, Toulouse, France
| | - Christophe Chevalier
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- Université Paris-Saclay, UVSQ, INRAE, VIM, Equipe Virus Influenza, Jouy-en-Josas, France
| | - Andrés Pizzorno
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- CIRI, Centre International de Recherche en Infectiologie (Team VirPath), Inserm U1111, Université Claude Bernard Lyon 1, CNRS UMR5308, ENS de Lyon, Lyon, France
| | - Ronan Le Goffic
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- Université Paris-Saclay, UVSQ, INRAE, VIM, Equipe Virus Influenza, Jouy-en-Josas, France
| | - Thibaut Crépin
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- Institut de Biologie Structurale (IBS), Université Grenoble Alpes, CEA, CNRS, Grenoble, France
| | - Gaëlle Simon
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- Swine Virology Immunology Unit, Ploufragan-Plouzané-Niort Laboratory, ANSES, Ploufragan, France
| | - Nadia Naffakh
- CNRS GDR2073 ResaFlu, Groupement de Recherche sur les Virus Influenza, France
- RNA Biology and Influenza Virus Unit, Institut Pasteur, CNRS UMR3569, Université de Paris, Paris, France
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23
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Valesano AL, Fitzsimmons WJ, Blair CN, Woods RJ, Gilbert J, Rudnik D, Mortenson L, Friedrich TC, O’Connor DH, MacCannell DR, Petrie JG, Martin ET, Lauring AS. SARS-CoV-2 Genomic Surveillance Reveals Little Spread From a Large University Campus to the Surrounding Community. Open Forum Infect Dis 2021; 8:ofab518. [PMID: 34805437 PMCID: PMC8600169 DOI: 10.1093/ofid/ofab518] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has had high incidence rates at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are poorly understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. METHODS We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan and the surrounding community during the Fall 2020 semester (August 16-November 24). We sequenced complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total cases in Washtenaw County over the study interval. RESULTS Phylogenetic analysis identified >200 introductions into the student population, most of which were not related to other student cases. There were 2 prolonged student transmission clusters, of 115 and 73 individuals, that spanned multiple on-campus residences. Remarkably, <5% of nonstudent genomes were descended from student clusters, and viral descendants of student cases were rare during a subsequent wave of infections in the community. CONCLUSIONS The largest outbreaks among students at the University of Michigan did not significantly contribute to the rise in community cases in Fall 2020. These results provide valuable insights into SARS-CoV-2 transmission dynamics at the regional level.
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Affiliation(s)
- Andrew L Valesano
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
| | - William J Fitzsimmons
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Christopher N Blair
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert J Woods
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Julie Gilbert
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Dawn Rudnik
- University Health Service, University of Michigan, Ann Arbor, Michigan, USA
| | - Lindsey Mortenson
- University Health Service, University of Michigan, Ann Arbor, Michigan, USA
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - David H O’Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Joshua G Petrie
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Emily T Martin
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Adam S Lauring
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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24
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Currie DW, Moreno GK, Delahoy MJ, Pray IW, Jovaag A, Braun KM, Cole D, Shechter T, Fajardo GC, Griggs C, Yandell BS, Goldstein S, Bushman D, Segaloff HE, Kelly GP, Pitts C, Lee C, Grande KM, Kita-Yarbro A, Grogan B, Mader S, Baggott J, Bateman AC, Westergaard RP, Tate JE, Friedrich TC, Kirking HL, O'Connor DH, Killerby ME. Interventions to Disrupt Coronavirus Disease Transmission at a University, Wisconsin, USA, August-October 2020. Emerg Infect Dis 2021; 27:2776-2785. [PMID: 34586058 PMCID: PMC8544969 DOI: 10.3201/eid2711.211306] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
University settings have demonstrated potential for coronavirus disease (COVID-19) outbreaks; they combine congregate living, substantial social activity, and a young population predisposed to mild illness. Using genomic and epidemiologic data, we describe a COVID-19 outbreak at the University of Wisconsin-Madison, Madison, Wisconsin, USA. During August-October 2020, a total of 3,485 students, including 856/6,162 students living in dormitories, tested positive. Case counts began rising during move-in week, August 25-31, 2020, then rose rapidly during September 1-11, 2020. The university initiated multiple prevention efforts, including quarantining 2 dormitories; a subsequent decline in cases was observed. Genomic surveillance of cases from Dane County, in which the university is located, did not find evidence of transmission from a large cluster of cases in the 2 quarantined dorms during the outbreak. Coordinated implementation of prevention measures can reduce COVID-19 spread in university settings and may limit spillover to the surrounding community.
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25
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Braun KM, Moreno GK, Buys A, Somsen ED, Bobholz M, Accola MA, Anderson L, Rehrauer WM, Baker DA, Safdar N, Lepak AJ, O’Connor DH, Friedrich TC. Viral Sequencing to Investigate Sources of SARS-CoV-2 Infection in US Healthcare Personnel. Clin Infect Dis 2021; 73:e1329-e1336. [PMID: 33857303 PMCID: PMC8083259 DOI: 10.1093/cid/ciab281] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Healthcare personnel (HCP) are at increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We posit that current infection control guidelines generally protect HCP from SARS-CoV-2 infection in a healthcare setting. METHODS In this retrospective case series, we used viral genomics to investigate the likely source of SARS-CoV-2 infection in HCP at a major academic medical institution in the Upper Midwest of the United States between 25 March and 27 December 2020. We obtained limited epidemiological data through informal interviews and review of the electronic health record and combined this information with healthcare-associated viral sequences and viral sequences collected in the broader community to infer the most likely source of infection in HCP. RESULTS We investigated SARS-CoV-2 infection clusters involving 95 HCP and 137 possible patient contact sequences. The majority of HCP infections could not be linked to a patient or coworker (55 of 95 [57.9%]) and were genetically similar to viruses circulating concurrently in the community. We found that 10.5% of HCP infections (10 of 95) could be traced to a coworker. Strikingly, only 4.2% (4 of 95) could be traced to a patient source. CONCLUSIONS Infections among HCP add further strain to the healthcare system and put patients, HCP, and communities at risk. We found no evidence for healthcare-associated transmission in the majority of HCP infections evaluated. Although we cannot rule out the possibility of cryptic healthcare-associated transmission, it appears that HCP most commonly become infected with SARS-CoV-2 via community exposure. This emphasizes the ongoing importance of mask wearing, physical distancing, robust testing programs, and rapid distribution of vaccines.
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Affiliation(s)
- Katarina M Braun
- Department of Pathobiological Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Gage K Moreno
- Department of Pathology and Laboratory Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Ashley Buys
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Elizabeth D Somsen
- Department of Pathology and Laboratory Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Max Bobholz
- Department of Pathology and Laboratory Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Molly A Accola
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Laura Anderson
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - William M Rehrauer
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - David A Baker
- Department of Pathology and Laboratory Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Nasia Safdar
- Department of Medicine, Division of Infectious Diseases, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alexander J Lepak
- Department of Medicine, Division of Infectious Diseases, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - David H O’Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Wisconsin National Primate Research Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Wisconsin National Primate Research Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
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26
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Braun KM, Moreno GK, Wagner C, Accola MA, Rehrauer WM, Baker DA, Koelle K, O’Connor DH, Bedford T, Friedrich TC, Moncla LH. Acute SARS-CoV-2 infections harbor limited within-host diversity and transmit via tight transmission bottlenecks. PLoS Pathog 2021; 17:e1009849. [PMID: 34424945 PMCID: PMC8412271 DOI: 10.1371/journal.ppat.1009849] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 09/02/2021] [Accepted: 07/29/2021] [Indexed: 02/08/2023] Open
Abstract
The emergence of divergent SARS-CoV-2 lineages has raised concern that novel variants eliciting immune escape or the ability to displace circulating lineages could emerge within individual hosts. Though growing evidence suggests that novel variants arise during prolonged infections, most infections are acute. Understanding how efficiently variants emerge and transmit among acutely-infected hosts is therefore critical for predicting the pace of long-term SARS-CoV-2 evolution. To characterize how within-host diversity is generated and propagated, we combine extensive laboratory and bioinformatic controls with metrics of within- and between-host diversity to 133 SARS-CoV-2 genomes from acutely-infected individuals. We find that within-host diversity is low and transmission bottlenecks are narrow, with very few viruses founding most infections. Within-host variants are rarely transmitted, even among individuals within the same household, and are rarely detected along phylogenetically linked infections in the broader community. These findings suggest that most variation generated within-host is lost during transmission.
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Affiliation(s)
- Katarina M. Braun
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Gage K. Moreno
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Cassia Wagner
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Molly A. Accola
- University of Wisconsin School of Medicine and Public Health and the William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America
| | - William M. Rehrauer
- University of Wisconsin School of Medicine and Public Health and the William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, United States of America
| | - David A. Baker
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Katia Koelle
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
| | - David H. O’Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Trevor Bedford
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Thomas C. Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Louise H. Moncla
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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27
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Moreno GK, Braun KM, Pray IW, Segaloff HE, Lim A, Poulsen K, Meiman J, Borcher J, Westergaard RP, Moll MK, Friedrich TC, O'Connor DH. Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in Intercollegiate Athletics Not Fully Mitigated With Daily Antigen Testing. Clin Infect Dis 2021; 73:S45-S53. [PMID: 33977295 PMCID: PMC8136076 DOI: 10.1093/cid/ciab343] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Background High-frequency, rapid-turnaround severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, 2 SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester, despite mandatory directly observed daily antigen testing. Methods During the fall 2020 semester, athletes and staff in both programs were tested daily using Quidel’s Sofia SARS Antigen Fluorescent Immunoassay, with positive antigen results requiring confirmatory testing with real-time reverse-transcription polymerase chain reaction. We used genomic sequencing to investigate transmission dynamics in these 2 outbreaks. Results In the first outbreak, 32 confirmed cases occurred within a university athletics program after the index patient attended a meeting while infectious, despite a negative antigen test on the day of the meeting. Among isolates sequenced from that outbreak, 24 (92%) of 26 were closely related, suggesting sustained transmission following an initial introduction event. In the second outbreak, 12 confirmed cases occurred among athletes from 2 university programs that faced each other in an athletic competition, despite receipt of negative antigen test results on the day of the competition. Sequences from both teams were closely related and distinct from viruses circulating in the community for team 1, suggesting transmission during intercollegiate competition in the community for team 2. Conclusions These findings suggest that antigen testing alone, even when mandated and directly observed, may not be sufficient as an intervention to prevent SARS-CoV-2 outbreaks in congregate settings, and they highlight the importance of vaccination to prevent SARS-CoV-2 outbreak in congregate settings.
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Affiliation(s)
- Gage K Moreno
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Katarina M Braun
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ian W Pray
- Wisconsin Department of Health Services, Madison, Wisconsin, USA.,Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Hannah E Segaloff
- Wisconsin Department of Health Services, Madison, Wisconsin, USA.,Epidemic Intelligence Service, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Ailam Lim
- Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Keith Poulsen
- Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jonathan Meiman
- Wisconsin Department of Health Services, Madison, Wisconsin, USA
| | - James Borcher
- Department of Family Medicine, Division of Sports Medicine, Ohio State University, Columbus Ohio, USA
| | - Ryan P Westergaard
- Wisconsin Department of Health Services, Madison, Wisconsin, USA.,Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Michael K Moll
- Athletic Department, University of Wisconsin-Madison, Madison, Wisconsin USA
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
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28
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Gutierrez B, Márquez S, Prado-Vivar B, Becerra-Wong M, Guadalupe JJ, da Silva Candido D, Fernandez-Cadena JC, Morey-Leon G, Armas-Gonzalez R, Andrade-Molina DM, Bruno A, de Mora D, Olmedo M, Portugal D, Gonzalez M, Orlando A, Drexler JF, Moreira-Soto A, Sander AL, Brünink S, Kühne A, Patiño L, Carrazco-Montalvo A, Mestanza O, Zurita J, Sevillano G, du Plessis L, McCrone JT, Coloma J, Trueba G, Barragán V, Rojas-Silva P, Grunauer M, Kraemer MU, Faria NR, Escalera-Zamudio M, Pybus OG, Cárdenas P. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.03.31.21254685. [PMID: 33851177 PMCID: PMC8043474 DOI: 10.1101/2021.03.31.21254685] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Characterisation of SARS-CoV-2 genetic diversity through space and time can reveal trends in virus importation and domestic circulation, and permit the exploration of questions regarding the early transmission dynamics. Here we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the COVID-19 pandemic. We generate and analyse 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylgeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions (NPIs), with differential degrees of persistence and national dissemination.
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Affiliation(s)
- Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK
- Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Quito, Ecuador
| | - Sully Márquez
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Belén Prado-Vivar
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Mónica Becerra-Wong
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Juan José Guadalupe
- Laboratorio de Biotecnología Vegetal, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Juan Carlos Fernandez-Cadena
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón, Ecuador
| | - Gabriel Morey-Leon
- Faculty of Medical Sciences, Universidad de Guayaquil, Guayaquil, Ecuador
| | - Rubén Armas-Gonzalez
- Faculty of Sciences, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador
| | - Derly Madeleiny Andrade-Molina
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón, Ecuador
| | - Alfredo Bruno
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
- Universidad Agraria del Ecuador
| | - Domenica de Mora
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Maritza Olmedo
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Denisse Portugal
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Manuel Gonzalez
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Alberto Orlando
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Jan Felix Drexler
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Andres Moreira-Soto
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Anna-Lena Sander
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Sebastian Brünink
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Arne Kühne
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Virology, Berlin, Germany
| | - Leandro Patiño
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | | | - Orson Mestanza
- Instituto Nacional de Investigación en Salud Pública, Guayaquil, Ecuador
| | - Jeannete Zurita
- Facultad de Medicina, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito, Ecuador
| | - Gabriela Sevillano
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito, Ecuador
| | | | - John T. McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Josefina Coloma
- School of Public Health, University of California, Berkeley, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | - Verónica Barragán
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Michelle Grunauer
- Escuela de Medicina, Universidad San Francisco de Quito, Quito, Ecuador
| | | | - Nuno R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London, UK
| | | | - Oliver G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
- Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK
| | - Paúl Cárdenas
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito, Ecuador
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29
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Moreno GK, Braun KM, Pray IW, Segaloff HE, Lim A, Poulson K, Meiman J, Borcher J, Westergaard RP, Moll MK, Friedrich TC, O'Connor DH. SARS-CoV-2 transmission in intercollegiate athletics not fully mitigated with daily antigen testing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021. [PMID: 33688665 PMCID: PMC7941640 DOI: 10.1101/2021.03.03.21252838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Background High frequency, rapid turnaround SARS-CoV-2 testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, two SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester despite mandatory directly observed daily antigen testing. Methods During the fall 2020 semester, athletes and staff in both programs were tested daily using Quidel's Sofia SARS Antigen Fluorescent Immunoassay (FIA), with positive antigen results requiring confirmatory testing with real-time reverse transcription polymerase chain reaction (RT-PCR). We used genomic sequencing to investigate transmission dynamics in these two outbreaks. Results In Outbreak 1, 32 confirmed cases occurred within a university athletics program after the index patient attended a meeting while infectious despite a negative antigen test on the day of the meeting. Among isolates sequenced from Outbreak 1, 24 (92%) of 26 were closely related, suggesting sustained transmission following an initial introduction event. In Outbreak 2, 12 confirmed cases occurred among athletes from two university programs that faced each other in an athletic competition despite receiving negative antigen test results on the day of the competition. Sequences from both teams were closely related and unique from strains circulating in the community, suggesting transmission during intercollegiate competition. Conclusions These findings suggest that antigen testing alone, even when mandated and directly observed, may not be sufficient as an intervention to prevent SARS-CoV-2 outbreaks in congregate settings, and highlights the importance of supplementing serial antigen testing with appropriate mitigation strategies to prevent SARS-CoV-2 outbreak in congregate settings. Summary High frequency, rapid turnaround SARS-CoV-2 testing continues to be proposed as a way of efficiently identifying and mitigating transmission in congregate settings. However, here we describe two SARS-CoV-2 outbreaks occurred among intercollegiate university athletic programs during the fall 2020 semester.
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Affiliation(s)
- Gage K Moreno
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison USA 53711
| | - Katarina M Braun
- Department of Pathobiological Sciences, University of Wisconsin-Madison USA 53711
| | - Ian W Pray
- Wisconsin Department of Health Services, USA 53703.,Epidemic Intelligence Service, Centers for Disease Control and Prevention USA 30333
| | - Hannah E Segaloff
- Wisconsin Department of Health Services, USA 53703.,Epidemic Intelligence Service, Centers for Disease Control and Prevention USA 30333
| | - Ailam Lim
- Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison USA 53711
| | - Keith Poulson
- Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison USA 53711
| | | | - James Borcher
- Department of Family Medicine, Division of Sports Medicine, Ohio State University USA 43210
| | - Ryan P Westergaard
- Wisconsin Department of Health Services, USA 53703.,Department of Medicine, University of Wisconsin-Madison, USA 53711
| | - Michael K Moll
- Athletic Department, University of Wisconsin-Madison USA 53711
| | - Thomas C Friedrich
- Department of Pathobiological Sciences, University of Wisconsin-Madison USA 53711
| | - David H O'Connor
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison USA 53711
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30
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Gutierrez B, Márquez S, Prado-Vivar B, Becerra-Wong M, Guadalupe JJ, Candido DDS, Fernandez-Cadena JC, Morey-Leon G, Armas-Gonzalez R, Andrade-Molina DM, Bruno A, De Mora D, Olmedo M, Portugal D, Gonzalez M, Orlando A, Drexler JF, Moreira-Soto A, Sander AL, Brünink S, Kühne A, Patiño L, Carrazco-Montalvo A, Mestanza O, Zurita J, Sevillano G, Du Plessis L, McCrone JT, Coloma J, Trueba G, Barragán V, Rojas-Silva P, Grunauer M, Kraemer MUG, Faria NR, Escalera-Zamudio M, Pybus OG, Cárdenas P. Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador. Virus Evol 2021; 7:veab051. [PMID: 34527281 PMCID: PMC8244811 DOI: 10.1093/ve/veab051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/12/2021] [Accepted: 06/03/2021] [Indexed: 12/23/2022] Open
Abstract
Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.
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Affiliation(s)
- Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Sully Márquez
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Belén Prado-Vivar
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Mónica Becerra-Wong
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Juan José Guadalupe
- Laboratorio de Biotecnología Vegetal, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | | | - Juan Carlos Fernandez-Cadena
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón 092301, Ecuador
| | - Gabriel Morey-Leon
- Faculty of Medical Sciences, Universidad de Guayaquil, Guayaquil 090613, Ecuador
| | - Rubén Armas-Gonzalez
- Faculty of Sciences, Escuela Superior Politécnica del Litoral, Guayaquil 090112, Ecuador
| | - Derly Madeleiny Andrade-Molina
- Omics Sciences Laboratory, Faculty of Medical Sciences, Universidad de Especialidades Espíritu Santo, Samborondón 092301, Ecuador
| | - Alfredo Bruno
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Domenica De Mora
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Maritza Olmedo
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Denisse Portugal
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Manuel Gonzalez
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Alberto Orlando
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | - Jan Felix Drexler
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Andres Moreira-Soto
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Anna-Lena Sander
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Sebastian Brünink
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Arne Kühne
- Institute of Virology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany
| | - Leandro Patiño
- Instituto Nacional de Investigación en Salud Pública, Guayaquil 3961, Ecuador
| | | | - Orson Mestanza
- Servicio de Genética, Instituto Nacional de Salud del Niño San Borja, Lima 15037, Perú
| | - Jeannete Zurita
- Facultad de Medicina, Pontificia Universidad Católica del Ecuador, Quito 170143, Ecuador
| | - Gabriela Sevillano
- Unidad de Investigaciones en Biomedicina, Zurita & Zurita Laboratorios, Quito 170104, Ecuador
| | - Louis Du Plessis
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - John T McCrone
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JW, UK
| | - Josefina Coloma
- School of Public Health, University of California, Berkeley CA 94704, USA
| | - Gabriel Trueba
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Verónica Barragán
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Patricio Rojas-Silva
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Michelle Grunauer
- Escuela de Medicina, Universidad San Francisco de Quito, Quito 170901, Ecuador
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | | | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, Oxfordshire OX1 3SY, UK
| | - Paúl Cárdenas
- Instituto de Microbiología, Universidad San Francisco de Quito, Quito 170901, Ecuador
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