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Abstract

Introduction

The SARS-CoV-2 lineages carrying the amino acid change D614G have become the dominant variants in the global COVID-19 pandemic. By June 2021, all the emerging variants of concern carried the D614G mutation. The rapid spread of the G614 mutant suggests that it may have a transmission advantage over the D614 wildtype.

Aim

Our objective was to estimate the transmission advantage of D614G by integrating phylogenetic and epidemiological analysis.

Methods

We assume that the mutation D614G was the only site of interest which characterised the two cocirculating virus strains by June 2020, but their differential transmissibility might be attributable to a combination of D614G and other mutations. We define the fitness of G614 as the ratio of the basic reproduction number of the strain with G614 to the strain with D614 and applied an epidemiological framework for fitness inference to analyse SARS-CoV-2 surveillance and sequence data.

Results

Using this framework, we estimated that the G614 mutant is 31% (95% credible interval: 28–34) more transmissible than the D614 wildtype. Therefore, interventions that were previously effective in containing or mitigating the D614 wildtype (e.g. in China, Vietnam and Thailand) may be less effective against the G614 mutant.

Conclusion

Our framework can be readily integrated into current SARS-CoV-2 surveillance to monitor the emergence and fitness of mutant strains such that pandemic surveillance, disease control and development of treatment and vaccines can be adjusted dynamically.

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/content/10.2807/1560-7917.ES.2021.26.49.2002005
2021-12-09
2024-12-21
/content/10.2807/1560-7917.ES.2021.26.49.2002005
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References

  1. Tang X, Wu C, Li X, Song Y, Yao X, Wu X, et al. On the origin and continuing evolution of SARS-CoV-2. Natl Sci Rev. 2020;7(6):1012-23.  https://doi.org/10.1093/nsr/nwaa036  PMID: 34676127 
  2. Global Initiative on Sharing All Influenza Data (GISAID). EpiCoV - Pandemic coronavirus causing COVID-19. Munich: GISAID. [Accessed: 30 Jun 2020] Available from: https://www.gisaid.org
  3. Zhang X, Tan Y, Ling Y, Lu G, Liu F, Yi Z, et al. Viral and host factors related to the clinical outcome of COVID-19. Nature. 2020;583(7816):437-40.  https://doi.org/10.1038/s41586-020-2355-0  PMID: 32434211 
  4. Korber B, Fischer WM, Gnanakaran S, Yoon H, Theiler J, Abfalterer W, et al. Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell. 2020;182(4):812-827.e19.  https://doi.org/10.1016/j.cell.2020.06.043  PMID: 32697968 
  5. COVID-19 Genomics UK Consortium (COG-UK). Updated analysis of SARS-CoV-2 spike protein variant D614G in the UK: evaluating evidence for effects on transmission and pathogenicity. Cambridge: COG-UK; 2020. Available from: https://www.cogconsortium.uk/wp-content/uploads/2020/07/25th-June-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf
  6. Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599-604.  https://doi.org/10.1098/rspb.2006.3754  PMID: 17476782 
  7. Li Q, Wu J, Nie J, Zhang L, Hao H, Liu S, et al. The impact of mutations in SARS-CoV-2 spike on viral infectivity and antigenicity. Cell. 2020;182(5):1284-1294.e9.  https://doi.org/10.1016/j.cell.2020.07.012  PMID: 32730807 
  8. Volz E, Hill V, McCrone JT, Price A, Jorgensen D, O’Toole Á, et al. Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity. Cell. 2021;184(1):64-75.e11.  https://doi.org/10.1016/j.cell.2020.11.020  PMID: 33275900 
  9. Volz EM, Hill V, McCrone JT, Price A, Jorgensen D, O'Toole A, et al. Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity. medRxiv. 2020.07.31.20166082. Preprint. https://doi.org/10.1101/2020.07.31.20166082
  10. Leung K, Lipsitch M, Yuen KY, Wu JT. Monitoring the fitness of antiviral-resistant influenza strains during an epidemic: a mathematical modelling study. Lancet Infect Dis. 2017;17(3):339-47.  https://doi.org/10.1016/S1473-3099(16)30465-0  PMID: 27914853 
  11. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490.  https://doi.org/10.1371/journal.pone.0009490  PMID: 20224823 
  12. Lam TT-Y. Tracking the genomic footprints of SARS-CoV-2 transmission. Trends Genet. 2020;36(8):544-6.  https://doi.org/10.1016/j.tig.2020.05.009  PMID: 32527617 
  13. Diekmann O, Heesterbeek JA, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-85.  https://doi.org/10.1098/rsif.2009.0386  PMID: 19892718 
  14. Leung K, Wu JT, Liu D, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet. 2020;395(10233):1382-93.  https://doi.org/10.1016/S0140-6736(20)30746-7  PMID: 32277878 
  15. Kwok KO, Wong VWY, Wei WI, Wong SYS, Tang JW-T. Epidemiological characteristics of the first 53 laboratory-confirmed cases of COVID-19 epidemic in Hong Kong, 13 February 2020. Euro Surveill. 2020;25(16):2000155.  https://doi.org/10.2807/1560-7917.ES.2020.25.16.2000155  PMID: 32347198 
  16. Backer JA, Klinkenberg D, Wallinga J. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro Surveill. 2020;25(5):2000062.  https://doi.org/10.2807/1560-7917.ES.2020.25.5.2000062  PMID: 32046819 
  17. Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis. 2020;20(6):669-77.  https://doi.org/10.1016/S1473-3099(20)30243-7  PMID: 32240634 
  18. Leung K, Wu JT, Xu K, Wein LM. No detectable surge in SARS-CoV-2 transmission attributable to the April 7, 2020 Wisconsin election. Am J Public Health. 2020;110(8):1169-70.  https://doi.org/10.2105/AJPH.2020.305770  PMID: 32552029 
  19. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020;584(7820):257-61.  https://doi.org/10.1038/s41586-020-2405-7  PMID: 32512579 
  20. COVID-19 Genomics UK (COG-UK) Consortium. SARS-CoV-2 genomic epidemiology in the UK. . Cambridge: COG-UK; 2020. Available from: https://www.cogconsortium.uk/wp-content/uploads/2020/06/28th-May-2020-Report-COVID-19-Genomics-UK-COG-UK-Consortium.pdf
  21. Britton T, Ball F, Trapman P. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2. Science. 2020;369(6505):846-9.  https://doi.org/10.1126/science.abc6810  PMID: 32576668 
  22. Tan W, Niu P, Zhao X, Pan Y, Zhang Y, Chen L, et al. Notes from the field: Reemergent cases of COVID-19—Xinfadi wholesales market, Beijing Municipality, China, June 11, 2020. China CDC Weekly. 2020:1-3. Available from: http://weekly.chinacdc.cn/en/article/doi/10.46234/ccdcw2020.132
  23. Chinese Center for Disease Control and Prevention (CCDC). Situation updates of Beijing's COVID-19 outbreak in June 2020. Beijing: CCDC; 2020. Available from: http://www.chinacdc.cn/yw_9324/202006/P020200626557038667020.pdf
  24. Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al. Characteristics of COVID-19 infection in Beijing. J Infect. 2020;80(4):401-6.  https://doi.org/10.1016/j.jinf.2020.02.018  PMID: 32112886 
  25. Wu JT, Leung K, Bushman M, Kishore N, Niehus R, de Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med. 2020;26(4):506-10.  https://doi.org/10.1038/s41591-020-0822-7  PMID: 32284616 
  26. Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 2020;20(5):553-8.  https://doi.org/10.1016/S1473-3099(20)30144-4  PMID: 32171059 
  27. Zhang J, Litvinova M, Liang Y, Wang Y, Wang W, Zhao S, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science. 2020;368(6498):1481-6.  https://doi.org/10.1126/science.abb8001  PMID: 32350060 
  28. He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26(5):672-5.  https://doi.org/10.1038/s41591-020-0869-5  PMID: 32296168 
  29. Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. Princeton: Princeton University Press; 2011.
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