1887

Microbial Genomics logo

Volume 8, Issue 9

Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants Open Access

Abstract

During the first semester of 2021, all of Brazil has suffered an intense wave of COVID-19 associated with the Gamma variant. In July, the first cases of Delta variant were detected in the state of Rio de Janeiro. In this work, we have employed phylodynamic methods to analyse more than 1 600 genomic sequences of Delta variant collected until September in Rio de Janeiro to reconstruct how this variant has surpassed Gamma and dispersed throughout the state. After the introduction of Delta, it has initially spread mostly in the homonymous city of Rio de Janeiro, the most populous of the state. In a second stage, dispersal occurred to mid- and long-range cities, which acted as new close-range hubs for spread. We observed that the substitution of Gamma by Delta was possibly caused by its higher viral load, a proxy for transmissibility. This variant turnover prompted a new surge in cases, but with lower lethality than was observed during the peak caused by Gamma. We reason that high vaccination rates in the state of Rio de Janeiro were possibly what prevented a higher number of deaths.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): COVID-19 , delta , dispersal , epidemiology , phylogeography , variant of concern and viral load
© 2022 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000859
2022-09-15
2024-05-14
Loading full text...

Full text loading...

/deliver/fulltext/mgen/8/9/mgen000859.html?itemId=/content/journal/mgen/10.1099/mgen.0.000859&mimeType=html&fmt=ahah

References

  1. Faria NR, Mellan TA, Whittaker C, Claro IM, Candido D da S et al. Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science 2021; 372:815–821 [View Article]
     [Google Scholar]
  2. Francisco Junior R da S, Lamarca AP, de Almeida LGP, Cavalcante L, Machado DT et al. Turnover of SARS-CoV-2 lineages shaped the pandemic and enabled the emergence of new variants in the state of Rio de Janeiro, Brazil. Viruses 2021; 13:2013 [View Article]
     [Google Scholar]
  3. Resende PC, Delatorre E, Gräf T, Mir D, Motta FC et al. Evolutionary dynamics and dissemination pattern of the SARS-CoV-2 lineage B.1.1.33 during the early pandemic phase in Brazil. Front Microbiol 2020; 11:615280 [View Article]
     [Google Scholar]
  4. Arantes I, Naveca FG, Gräf T, Miyajima F, Faoro H et al. Emergence and spread of the SARS-CoV-2 variant of concern delta across different Brazilian Regions. Genetic and Genomic Medicine 2021 [View Article]
     [Google Scholar]
  5. Naveca FG, Valdinete N, Victor S, André de L, Fernanda N et al. The SARS-CoV-2 variant delta displaced the variants gamma and gamma plus in Amazonas, Brazil; 2021 https://virological.org/t/the-sars-cov-2-variant-delta-displaced-the-variants-gamma-and-gamma-plus-in-amazonas-brazil/765
  6. Patané J, Viala V, Lima L, Martins A, Barros C et al. SARS-CoV-2 Delta variant of concern in Brazil - multiple introductions, communitary transmission, and early signs of local evolution. Genetic and Genomic Medicine 2021 [View Article]
     [Google Scholar]
  7. Lamarca AP, de Almeida LGP, da Silva Francisco R, Cavalcante L, Machado DT et al. Genomic surveillance tracks the first community outbreak of the SARS-CoV-2 delta (B.1.617.2) variant in Brazil. J Virol 2021; 96:e0122821 [View Article]
     [Google Scholar]
  8. Instituto Brasileiro de Geografia e Estatística. “Rio de Janeiro.”; 2021 https://www.ibge.gov.br/cidades-e-estados/rj.html
  9. “Human Development Index, Technical Notes.” United Nations Development Programme; 2020 https://hdr.undp.org/sites/default/files/hdr2020_technical_notes.pdf
  10. Programa das Nações Unidas para o Desenvolvimento. “Ranking IDHM Unidades da Federação 2010.”; 2013 https://www.br.undp.org/content/brazil/pt/home/idh0/rankings/idhm-uf-2010.html
  11. Alpert T, Brito AF, Lasek-Nesselquist E, Rothman J, Valesano AL et al. Early introductions and transmission of SARS-CoV-2 variant B.1.1.7 in the United States. Cell 2021; 184:2595–2604 [View Article]
     [Google Scholar]
  12. Boterman WR. Urban-rural polarisation in times of the corona outbreak? The early demographic and geographic patterns of the SARS-CoV-2 epidemic in the Netherlands. Tijdschr Econ Soc Geogr 2020; 111:513–529 [View Article] [PubMed]
     [Google Scholar]
  13. Chen K, Li Z. The spread rate of SARS-CoV-2 is strongly associated with population density. J Travel Med 2020; 27:8 [View Article] [PubMed]
     [Google Scholar]
  14. Eilersen A, Sneppen K. SARS-CoV-2 superspreading in cities vs the countryside. APMIS 2021; 129:401–407 [View Article] [PubMed]
     [Google Scholar]
  15. Fauver JR, Petrone ME, Hodcroft EB, Shioda K, Ehrlich HY et al. Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell 2020; 181:990–996 [View Article] [PubMed]
     [Google Scholar]
  16. Fortaleza C, Guimarães RB, Rafael de CC, Ferreira CP, Gabriel Berg de A et al. The use of health geography modeling to understand early dispersion of COVID-19 in São paulo, Brazil. PLoS One 2021; 16:e0245051
     [Google Scholar]
  17. Kraemer MUG, Hill V, Ruis C, Dellicour S, Bajaj S et al. Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science 2021; 373:889–895 [View Article] [PubMed]
     [Google Scholar]
  18. Smith TP, Flaxman S, Gallinat AS, Kinosian SP, Stemkovski M et al. Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions. Proc Natl Acad Sci U S A 2021; 118:e2019284118 [View Article] [PubMed]
     [Google Scholar]
  19. Cota W. Monitoring the number of COVID-19 cases and deaths in Brazil at municipal and federative units level; 2020 [View Article]
  20. Nitahara A. População enfrenta dificuldades para fazer teste de covid-19 no Rio Agência Brasil; 2022 https://agenciabrasil.ebc.com.br/saude/noticia/2022-01/populacao-enfrenta-dificuldades-para-fazer-teste-de-covid-19-no-rio
  21. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001; 25:402–408 [View Article]
     [Google Scholar]
  22. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 2013; 30:772–780 [View Article] [PubMed]
     [Google Scholar]
  23. Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file anipulation. PLoS One 2016; 11:e0163962 [View Article]
     [Google Scholar]
  24. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 2020; 37:1530–1534 [View Article]
     [Google Scholar]
  25. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 2017; 14:587–589 [View Article] [PubMed]
     [Google Scholar]
  26. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol 2018; 35:518–522 [View Article]
     [Google Scholar]
  27. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 2018; 34:4121–4123 [View Article] [PubMed]
     [Google Scholar]
  28. Dellicour S, Gill MS, Faria NR, Rambaut A, Pybus OG et al. Relax, keep walking - a practical guide to continuous phylogeographic inference with beast. Mol Biol Evol 2021; 38:3486–3493 [View Article] [PubMed]
     [Google Scholar]
  29. Dellicour S, Rose R, Faria NR, Lemey P, Pybus OG. SERAPHIM: studying environmental rasters and phylogenetically informed movements. Bioinformatics 2016; 32:3204–3206 [View Article] [PubMed]
     [Google Scholar]
  30. Sagulenko P, Puller V, Neher RA. TreeTime: maximum-likelihood phylodynamic analysis. Virus Evol 2018; 4:vex042 [View Article]
     [Google Scholar]
  31. Moreira FRR, D’arc M, Mariani D, Herlinger AL, Schiffler FB et al. Epidemiological dynamics of SARS-CoV-2 VOC gamma in Rio de Janeiro, Brazil. Virus Evol 2021; 7: [View Article]
     [Google Scholar]
  32. Ong SWX, Chiew CJ, Ang LW, Mak T-M, Cui L et al. Clinical and virological features of SARS-CoV-2 variants of concern: a retrospective cohort study comparing B.1.1.7 (Alpha), B.1.315 (Beta), and B.1.617.2 (Delta). Clin Infect Dis 2021ciab721 [View Article]
     [Google Scholar]
  33. Twohig KA, Nyberg T, Zaidi A, Thelwall S, Sinnathamby MA et al. Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study. Lancet Infect Dis 2022; 22:35–42 [View Article]
     [Google Scholar]
  34. Francisco Junior R da S, de Almeida LGP, Lamarca AP, Cavalcante L, Martins Y et al. Emergence of within-host SARS-CoV-2 recombinant genome after coinfection by gamma and delta variants: a case report. Front Public Health 2022; 10:849978 [View Article]
     [Google Scholar]
  35. Garvin MR, Prates ET, Romero J, Cliff A, Machado Gazolla JGF et al. Rapid expansion of SARS-CoV-2 variants of concern is a result of adaptive epistasis. Evol Biol 2021 [View Article]
     [Google Scholar]
  36. Jackson B, Boni MF, Bull MJ, Colleran A, Colquhoun RM et al. Generation and transmission of interlineage recombinants in the SARS-CoV-2 pandemic. Cell 2021; 184:5179–5188 [View Article] [PubMed]
     [Google Scholar]
  37. Turkahia Y, Thornlow B, Hinrichs A, McBroome J, Ayala N et al. Pandemic-Scale phylogenomics reveals elevated recombination rates in the SARS-CoV-2 spike region. Evol Biol 2021 [View Article]
     [Google Scholar]
  38. World Health Organization (WHO) Tracking SARS-CoV-2 Variants; 2021aMay31 https://www.who.int/activities/tracking-SARS-CoV-2-variants
  39. World Health Organization (WHO) WHO Announces Simple, Easy-to-Say Labels for SARS-CoV-2 Variants of Interest and Concern; 2021bMay31 http://www.who.int/news/item/31-05-2021-who-announces-simple-easy-to-say-labels-for-sars-cov-2-variants-of-interest-and-concern
  40. Cini Oliveira M, de Araujo Eleuterio T, de Andrade Corrêa AB, da Silva LDR, Rodrigues RC et al. Fatores associados ao óbito em casos confirmados de COVID-19 no estado do Rio de Janeiro. BMC Infect Dis 2021; 21:687 [View Article] [PubMed]
     [Google Scholar]
  41. Ferreira DSR, Ferreira PF, Oliveira PSL, Ribeiro J, Goncalves EAS et al. Temporal and spatial characteristics of the spread of COVID-19 in Rio De Janeiro state and city. MedRxiv 2020
     [Google Scholar]
  42. Liu Y, Rocklöv J. The reproductive number of the Delta variant of SARS-CoV-2 is far higher compared to the ancestral SARS-CoV-2 virus. J Travel Med 2021; 28: [View Article]
     [Google Scholar]
  43. Mlcochova P, Kemp SA, Dhar MS, Papa G, Meng B et al. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature 2021; 599:114–119 [View Article] [PubMed]
     [Google Scholar]
  44. Bolze A, Cirulli ET, Luo S, White S, Wyman D et al. Rapid displacement of SARS-CoV-2 Variant B.1.1.7 by B.1.617.2 and P.1 in the United States; 2021 https://doi.org/10.1101/2021.06.20.21259195
  45. Shen L, Triche TJ, Bard JD, Biegel JA, Judkins AR et al. Spike Protein NTD mutation G142D in SARS-CoV-2 Delta VOC lineages is associated with frequent back mutations, increased viral loads, and immune evasion. Infectious Diseases (except HIV/AIDS) 2021 [View Article]
     [Google Scholar]
  46. Goyal A, Reeves DB, Cardozo-Ojeda EF, Schiffer JT, Mayer BT. Viral load and contact heterogeneity predict SARS-CoV-2 transmission and super-spreading events. Elife 2021; 10:e63537 [View Article] [PubMed]
     [Google Scholar]
  47. de Souza UJB, Dos Santos RN, de Melo FL, Belmok A, Galvão JD et al. Genomic epidemiology of SARS-CoV-2 in Tocantins State and the Diffusion of P.1.7 and AY.99.2 Lineages in Brazil. Viruses 2022; 14:659 [View Article]
     [Google Scholar]
  48. Waters JM, Fraser CI, Hewitt GM. Founder takes all: density-dependent processes structure biodiversity. Trends Ecol Evol 2013; 28:78–85 [View Article] [PubMed]
     [Google Scholar]
  49. Bolze A, Luo S, White S, Cirulli ET, Wyman D et al. SARS-CoV-2 variant delta rapidly displaced variant lpha in the United States and led to higher viral loads. Cell Rep Med 2022; 3:100564 [View Article] [PubMed]
     [Google Scholar]
  50. Pavelka M, Van-Zandvoort K, Abbott S, Sherratt K, Majdan M. CMMID COVID-19 working group, inštitút zdravotných analýz, et al. “The Impact of Population-Wide Rapid Antigen Testing on SARS-CoV-2 Prevalence in Slovakia” 2021; 372:635–641 [View Article]
     [Google Scholar]
  51. Peeling RW, Wedderburn CJ, Garcia PJ, Boeras D, Fongwen N et al. Serology testing in the COVID-19 pandemic response. Lancet Infect Dis 2020; 20:e245–e249 [View Article] [PubMed]
     [Google Scholar]
  52. Thanh TT, Nhan NTT, Mai HK, Trieu NB, Huy LX et al. The application of sample pooling for mass screening of SARS-CoV-2 in an outbreak of COVID-19 in Vietnam. Am J Trop Med Hyg 2021; 104:1531–1534 [View Article]
     [Google Scholar]
  53. Wilmes P, Zimmer J, Schulz J, Glod F, Veiber L et al. SARS-CoV-2 transmission risk from asymptomatic carriers: results from a mass screening programme in Luxembourg. Lancet Reg Health Eur 2021; 4:100056 [View Article] [PubMed]
     [Google Scholar]
  54. Bagheri G, Thiede B, Hejazi B, Schlenczek O, Bodenschatz E. An upper bound on one-to-one exposure to infectious human respiratory particles. Proc Natl Acad Sci U S A 2021; 118:e2110117118 [View Article] [PubMed]
     [Google Scholar]
  55. Brooks JT, Butler JC. Effectiveness of mask wearing to control community spread of SARS-CoV-2. JAMA 2021; 325:998–999 [View Article] [PubMed]
     [Google Scholar]
  56. Ferris M, Ferris R, Workman C, O’Connor E, Enoch DA et al. FFP3 respirators protect healthcare workers against infection with SARS-CoV-2. Authorea Preprints 2021 [View Article]
     [Google Scholar]
  57. Li Y, Undurraga EA, Zubizarreta JR. Effectiveness of localized lockdowns in the COVID-19 pandemic. bioRxiv medRxiv 2020 [View Article]
     [Google Scholar]
  58. Olney AM, Smith J, Sen S, Thomas F, Unwin HJT. Estimating the effect of social distancing interventions on COVID-19 in the United States. Am J Epidemiol 2021; 190:1504–1509 [View Article]
     [Google Scholar]
  59. Zhang J, Litvinova M, Liang Y, Zheng W, Shi H et al. The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China. Sci Adv 2021; 7: [View Article]
     [Google Scholar]
  60. Costa GS, Cota W, Ferreira SC. Outbreak diversity in epidemic waves propagating through distinct geographical scales. Phys Rev Research 2020; 2: [View Article]
     [Google Scholar]
  61. Kishore N, Kahn R, Martinez PP, De Salazar PM, Mahmud AS et al. Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2. Sci Rep 2021; 11:6995 [View Article]
     [Google Scholar]
  62. Mazzoli M, Emanuele P, David M, Ciro C, Laetitia G et al. Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact. PLoS Comput Biol 2021; 17:e1009326
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000859
Loading
Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants
M Gen 8, 000859 (2022); https://doi.org/10.1099/mgen.0.000859
/content/journal/mgen/10.1099/mgen.0.000859
/content/journal/mgen/10.1099/mgen.0.000859
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Supplementary material 2

EXCEL

Supplementary material 3

EXCEL

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error