Science - USA (2021-07-09)

(Antfer) #1
laboratory evaluation study.Lancet Microbe10.1016/S2666-
5247(21)00056-2 (2021). doi:10.1001/jamapediatrics.2020.3988;
pmid: 32857112


  1. M. S. Hanet al., Clinical Characteristics and Viral RNA
    Detection in Children With Coronavirus Disease, 2019 in the
    Republic of Korea.JAMA Pediatr. 175 , 73–80 (2021).
    doi:10.1001/jamapediatrics.2020.3988; pmid: 32857112

  2. C. A. Pierceet al., Natural mucosal barriers and COVID-19 in
    children.JCI Insight 6 , e148694 (2021). doi:10.1172/
    jci.insight.148694; pmid: 33822777

  3. Q. Liet al., Early Transmission Dynamics in Wuhan, China, of
    Novel Coronavirus-Infected Pneumonia.N. Engl. J. Med. 382 ,
    1199 – 1207 (2020). doi:10.1056/NEJMoa2001316;
    pmid: 31995857

  4. L. Ferrettiet al., The timing of COVID-19 transmission.
    medRxiv [preprint]. 7 September 2020. pmid: 20188516

  5. X. Heet al., Temporal dynamics in viral shedding
    and transmissibility of COVID-19.Nat. Med. 26 ,
    672 – 675 (2020). doi:10.1038/s41591-020-0869-5;
    pmid: 32296168

  6. C. McAloonet al., Incubation period of COVID-19: A rapid
    systematic review and meta-analysis of observational research.
    BMJ Open 10 , e039652 (2020). doi:10.1136/bmjopen-2020-
    039652 ; pmid: 32801208

  7. P. Banka, C. Comiskey, The incubation period of COVID-19:
    A scoping review and meta-analysis to aid modelling
    and planning. MedRxiv 20216143 [preprint].
    3November2020.

  8. B. Rai, A. Shukla, L. K. Dwivedi, Incubation period for COVID-19:
    A systematic review and meta-analysis.J. Public Health
    10.1007/s10389-021-01478-1 (2021). doi:10.1007/s10389-
    021-01478-1; pmid: 33643779

  9. J. Bullardet al., Predicting infectious SARS-CoV-2 from
    diagnostic samples.Clin. Infect. Dis. 71 , 2663–2666 (2020).
    doi:10.1093/cid/ciaa638

  10. M. M. Aronset al., Presymptomatic SARS-CoV-2 Infections and
    Transmission in a Skilled Nursing Facility.N. Engl. J. Med. 382 ,
    2081 – 2090 (2020). doi:10.1056/NEJMoa2008457;
    pmid: 32329971

  11. R. Challenet al., Risk of mortality in patients infected with
    SARS-CoV-2 variant of concern 202012/1: Matched cohort
    study.BMJ 372 , n579 (2021). pmid: 33687922

  12. K. R. W. Emary, T. Golubchik, Efficacy of ChAdOx1 nCoV-19
    (AZD1222) vaccine against SARS-CoV-2 VOC 202012/01
    (B.1.1.7). SSRN [preprint]. 4 February 2021;https://ssrn.com/
    abstract=3779160.

  13. M. D. Parkeret al., Altered subgenomic RNA expression in
    SARS-CoV-2 B.1.1.7 infections. bioRxiv 433156 [preprint].
    4 March 2021. pmid: 433156

  14. M. Kiddet al., S-variant SARS-CoV-2 lineage B1.1.7 is
    associated with significantly higher viral loads in samples
    tested by TaqPath Polymerase Chain Reaction.J.Infect.Dis.
    jiab082 (2021). doi:10.1093/infdis/jiab082;
    pmid: 33580259

  15. T. Golubchiket al., COVID-19 Genomics UK (COG-UK)
    Consortium, Early analysis of a potential link between viral load
    and the N501Y mutation in the SARS-COV-2 spike protein.
    medRxiv 20249080 [preprint]. 15 January 2021.
    pmid: 20249080

  16. S. Kissleret al.,“Densely sampled viral trajectories suggest
    longer duration of acute infection with B.1.1.7 variant relative to
    non-B.1.1.7 SARS-CoV-2”(Harvard T. H. Chan School of
    Public Health, 2021);https://dash.harvard.edu/handle/1/
    37366884.

  17. Public Health England,“Investigation of novel
    SARS-CoV-2 Variant of Concern 202012/01: Technical
    briefing 5”(2021).

  18. K. Leung, M. H. Shum, G. M. Leung, T. T. Lam, J. T. Wu, Early
    transmissibility assessment of the N501Y mutant strains of
    SARS-CoV-2 in the United Kingdom, October to November
    2020.Euro Surveill. 26 , (2021). doi:10.2807/1560-7917.
    ES.2020.26.1.2002106; pmid: 33413740

  19. N. G. Davieset al., Estimated transmissibility and impact of
    SARS-CoV-2 lineage B.1.1.7 in England.Science 372 ,
    eabg3055 (2021). doi:10.1126/science.abg3055;
    pmid: 33658326

  20. R. Liet al., Substantial undocumented infection facilitates the
    rapid dissemination of novel coronavirus (SARS-CoV-2).
    Science 368 , 489–493 (2020). doi:10.1126/science.abb3221;
    pmid: 32179701

  21. Public Health England,“Investigation of novel SARS-CoV-2
    Variant of Concern 202012/01: Technical briefing 1”
    (2020).
    58. P.-C. Bürkner, brms: An R Package for Bayesian Multilevel
    Models Using Stan.J. Stat. Softw. 80 , (2017). doi:10.18637/
    jss.v080.i01
    59. P.-C. Bürkner, Advanced Bayesian Multilevel Modeling with
    the R Package brms.R J. 10 , 395 (2018). doi:10.32614/RJ-
    2018-017
    60. R Core Team,R: A Language and Environment for Statistical
    Computing(R Foundation for Statistical Computing, 2020);
    http://www.R-project.org/.
    61. K. Basileet al., Cell-based culture of SARS-CoV-2 informs
    infectivity and safe de-isolation assessments during COVID-19.
    Clin. Infect. Dis.ciaa1579 (2020). doi:10.1093/cid/ciaa1579;
    pmid: 33098412
    62. B. La Scolaet al., Viral RNA load as determined by cell culture
    as a management tool for discharge of SARS-CoV-2 patients
    from infectious disease wards.Eur. J. Clin. Microbiol. Infect. Dis.
    39 , 1059–1061 (2020). doi:10.1007/s10096-020-03913-9;
    pmid: 32342252
    63. J. J. A. van Kampenet al., Duration and key determinants of
    infectious virus shedding in hospitalized patients with
    coronavirus disease-2019 (COVID-19).Nat. Commun. 12 , 267
    (2021). doi:10.1038/s41467-020-20568-4; pmid: 33431879
    64. M. Wideraet al., Surveillance of SARS-CoV-2 in Frankfurt am
    Main from October to December 2020 Reveals High Viral
    Diversity Including Spike Mutation N501Y in B.1.1.70 and
    B.1.1.7.Microorganisms 9 , 748 (2021). doi:10.3390/
    microorganisms9040748; pmid: 33918332
    65. T. Toptanet al., Evaluation of a SARS-CoV-2 rapid antigen test:
    Potential to help reduce community spread?J. Clin. Virol. 135 ,
    104713 (2021). doi:10.1016/j.jcv.2020.104713;
    pmid: 33352470
    66. S. Herberhold, A.-M. Eis-Hübinger, M. Panning, Frequent
    detection of respiratory viruses by real-time PCR in adenoid
    samples from asymptomatic children.J. Clin. Microbiol. 47 ,
    2682 – 2683 (2009). doi:10.1128/JCM.00899-09;
    pmid: 19494063
    67. F. M. Liottiet al., Assessment of SARS-CoV-2 RNA Test Results
    Among Patients Who Recovered From COVID-19 With Prior
    Negative Results.JAMA Intern. Med. 181 , 702–704 (2021).
    doi:10.1001/jamainternmed.2020.7570; pmid: 33180119
    68. R. L. Tillettet al., Genomic evidence for reinfection with
    SARS-CoV-2: A case study.Lancet Infect. Dis. 21 , 52–58 (2021).
    doi:10.1016/S1473-3099(20)30764-7; pmid: 33058797
    69. K. K.-W. Toet al., Coronavirus Disease 2019 (COVID-19)
    Re-infection by a Phylogenetically Distinct Severe Acute
    Respiratory Syndrome Coronavirus 2 Strain Confirmed by
    Whole Genome Sequencing.Clin. Infect. Dis. 9 , 1664 (2020).
    70. P. Simmonds, S. Williams, H. Harvala, Understanding the
    outcomes of COVID-19–does the current model of an acute
    respiratory infection really fit?J. Gen. Virol. 102 , 10.1099/
    jgv.0.001545 (2020). doi:10.1099/jgv.0.001545; pmid: 33331810
    71. M. Perry, simanneal: A Python Module for Simulated Annealing
    Optimization;https://github.com/perrygeo/simanneal.
    72. Stan Development Team, Stan Modeling Language Users Guide
    and Reference Manual (version 2.25);https://mc-stan.org.
    73. Y. Liuet al., Viral dynamics in mild and severe cases of
    COVID-19.Lancet Infect. Dis. 20 , 656–657 (2020).
    doi:10.1016/S1473-3099(20)30232-2; pmid: 32199493
    74. K. K.-W. Toet al., Temporal profiles of viral load in posterior
    oropharyngeal saliva samples and serum antibody responses
    during infection by SARS-CoV-2: An observational cohort
    study.Lancet Infect. Dis. 20 , 565–574 (2020). doi:10.1016/
    S1473-3099(20)30196-1; pmid: 32213337
    75. S. Greenland, Principles of multilevel modelling.Int. J. Epidemiol.
    29 , 158–167 (2000). doi:10.1093/ije/29.1.158; pmid: 10750618
    76. F. Kurthet al., Studying the pathophysiology of coronavirus
    disease 2019: A protocol for the Berlin prospective COVID-19
    patient cohort (Pa-COVID-19).Infection 48 , 619–626 (2020).
    doi:10.1007/s15010-020-01464-x; pmid: 32535877
    77. C. Thibeaultet al., Clinical and virological characteristics of
    hospitalised COVID-19 patients in a German tertiary care
    centre during the first wave of the SARS-CoV-2 pandemic:
    A prospective observational study.Infection10.1007/s15010-
    021-01594-w (2021). doi:10.1007/s15010-021-01594-w;
    pmid: 33890243
    78. P. Virtanenet al., SciPy 1.0: Fundamental algorithms for
    scientific computing in Python.Nat. Methods 17 , 261– 272
    (2020). doi:10.1038/s41592-019-0686-2; pmid: 32015543
    79. W. McKinney, Data Structures for Statistical Computing in
    Python. InProceedings of the 9th Python in Science Conference
    (2010). doi:10.25080/majora-92bf1922-00a
    80. S. Seabold, J. Perktold, Statsmodels: Econometric and
    Statistical Modeling with Python. InProceedings of the


9th Python in Science Conference(2010). doi:10.25080/
majora-92bf1922-011


  1. J. D. Hunter, Matplotlib: A 2D Graphics Environment.Comput.
    Sci. Eng. 9 , 90–95 (2007). doi:10.1109/MCSE.2007.55

  2. T. Oliphant,Guide to NumPy(CreateSpace, ed. 2, 2015).

  3. M. Parker, seaborn_sinaplot;https://github.com/mparker2/
    seaborn_sinaplot.

  4. M. Waskomet al., seaborn: v0.5.0 (2014); DOI: 10.5281/
    zenodo.12710.

  5. B. Langmead, S. L. Salzberg, Fast gapped-read alignment with
    Bowtie 2.Nat. Methods 9 , 357–359 (2012). doi:10.1038/
    nmeth.1923; pmid: 22388286

  6. P. Daneceket al., Twelve years of SAMtools and BCFtools.
    Gigascience 10 , giab008 (2021). doi:10.1093/gigascience/
    giab008; pmid: 33590861

  7. J. K. Bonfieldet al., HTSlib: C library for reading/writing
    high-throughput sequencing data.Gigascience 10 ,
    giab007 (2021). doi:10.1093/gigascience/giab007;
    pmid: 33594436

  8. G. Dick,Genomic Approaches in Earth and Environmental
    Sciences(Wiley, 2018).

  9. N. D. Grubaughet al., An amplicon-based sequencing
    framework for accurately measuring intrahost virus diversity
    using PrimalSeq and iVar.Genome Biol. 20 , 8 (2019).
    doi:10.1186/s13059-018-1618-7; pmid: 30621750

  10. K. Katoh, D. M. Standley, MAFFT multiple sequence alignment
    software version 7: Improvements in performance and
    usability.Mol. Biol. Evol. 30 , 772–780 (2013). doi:10.1093/
    molbev/mst010; pmid: 23329690

  11. B. Goodrich, J. Gabry, I. Ali, S. Brilleman, rstanarm: Bayesian
    applied regression modeling via Stan (2020);https://mc-stan.
    org/rstanarm.

  12. B. Carpenteret al., Stan: A Probabilistic Programming
    Language.J. Stat. Softw. 76 , (2017). doi:10.18637/jss.v076.i01

  13. M. Dowle, A. Srinivasan, data.table: Extension of‘data.frame’
    (2020).

  14. H. Wickham,ggplot2: Elegant Graphics for Data Analysis
    (Springer, 2016).

  15. O. Tange, GNU Parallel 20201122 (‘Biden’) (2020);
    http://www.gnu.org/software/parallel/.

  16. H. B. Mann, D. R. Whitney, On a Test of Whether One of
    Two Random Variables is Stochastically Larger than the
    Other.Ann. Math. Stat. 18 , 50–60 (1947). doi:10.1214/aoms/
    1177730491

  17. Additional statistical information and the R code and data to
    reproduce the results, figures, and tables are available at
    https://doi.org/10.5281/zenodo.4774226.


ACKNOWLEDGMENTS
Computation was performed on the HPC for Research/Clinic
cluster of the Berlin Institute of Health, supported by D. Beule,
M. Holtgrewe, and O. Stolpe. We thank U. Gieraths and
L. Meiners for careful commentary on the manuscript,
T. D. Best for compiling cell culture isolation data, the Charité–
Universitätsmedizin Pa-COVID-19 collaborative study group for
providing additional onset of symptoms data, and S. Kissler for
providing additional details regarding their NBA study. The
conditions allowing the work to be done with no need for consent
are given athttps://gesetze.berlin.de/bsbe/document/jlr-
KHGBE2011V4P25.Funding:Work at Charité–Universitätsmedizin
Institute of Virology is funded by European Commission via project
ReCoVer, German Federal Ministry of Education and Research
(Bundesministerium für Bildung und Forschung) through projects
DZIF (301-4-7-01.703) to C.D.; VARIPath (01KI2021) to V.M.C.;
PROVID (FKZ 01KI20160C) to C.D., V.M.C., and L.E.S.; and
NaFoUniMedCovid19 (NUM)–COVIM (FKZ 01KX2021) to C.D.,
V.M.C., and L.E.S. The Pa-COVID 19 Study is supported by grants
from the Berlin Institute of Health. This study was supported in
part by the German Ministry of Health (Konsiliarlabor für
Coronaviren and SeCoV) to C.D. and V.M.C. T.C.J. is in part
funded through NIAID-NIH CEIRS contract HHSN272201400008C.
Author contributions:T.C.J., G.B., B.M.: bioinformatic processing,
statistical analysis, interpretation of results, writing of original
draft and final text; T.V.: statistical analysis, interpretation of
results, writing of original draft and final text, next-generation
sequencing; J.S., J.B.-S., T.B., J.T., M.L.S.: sample preparation,
virus isolation and culturing, RT-PCR, next-generation sequencing;
L.E.S., F.K.: collection of symptom onset data; P.M., R.S., M.Z.,
J.H., A.K., A.S., A.E.: diagnostic work and collection of raw data;
V.M.C.: diagnostic data collection, viral load calibration, supervision
of laboratory work, interpretation of results; C.D.: project concept,
interpretation of results, writing of original draft and final text.
Competing interests:The authors declare that they have no

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