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Cited by 1,461SARS-CoV-2 and COVID-19 ResearchCOVID-19 epidemiological studiesEvolution and Genetic Dynamics

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Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil

Open access
Faria, Nuno R., Mellan, Thomas A., Whittaker, Charles, Claro, Ingra M., Candido, Darlan da S., Mishra, Swapnil, Crispim, Myuki A. E., Sales, Flavia C. S., Hawryluk, Iwona, McCrone, John T., Hulswit, Ruben J. G., Franco, Lucas A. M., Ramundo, Mariana S., de Jesus, Jaqueline G., Andrade, Pamela S., Coletti, Thais M., Ferreira, Giulia M., Silva, Camila A. M., Manuli, Erika R., Pereira, Rafael H. M., Peixoto, Pedro S., Kraemer, Moritz U. G., Gaburo, Nelson, Camilo, Cecilia da C., Hoeltgebaum, Henrique, Souza, William M., Rocha, Esmenia C., de Souza, Leandro M., de Pinho, Mariana C., Araujo, Leonardo J. T., Malta, Frederico S. V., de Lima, Aline B., Silva, Joice do P., Zauli, Danielle A. G., Ferreira, Alessandro C. de S., Schnekenberg, Ricardo P., Laydon, Daniel J., Walker, Patrick G. T., Schlüter, Hannah M., dos Santos, Ana L. P., Vidal, Maria S., Del Caro, Valentina S., Filho, Rosinaldo M. F., dos Santos, Helem M., Aguiar, Renato S., Proença-Modena, José L., Nelson, Bruce, Hay, James A., Monod, Mélodie, Miscouridou, Xenia, Coupland, Helen, Sonabend, Raphael, Vollmer, Michaela, Gandy, Axel, Prete, Carlos A., Nascimento, Vitor H., Suchard, Marc A., Bowden, Thomas A., Pond, Sergei L. K., Wu, Chieh-Hsi, Ratmann, Oliver, Ferguson, Neil M., Dye, Christopher, Loman, Nick J., Lemey, Philippe, Rambaut, Andrew, Fraiji, Nelson A., Carvalho, Maria do P. S. S., Pybus, Oliver G., Flaxman, Seth, Bhatt, Samir, Sabino, Ester C.·2021·Science
DOI 10.1126/science.abh2644PMC PMC8139423

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Abstract

Unmitigated spread in Brazil: Despite an extensive network of primary care availability, Brazil has suffered profoundly during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Using daily data from state health offices, Castro et al. analyzed the pattern of spread of COVID-19 cases and deaths in the country from February to October 2020. Clusters of deaths before cases became apparent indicated unmitigated spread. SARS-CoV-2 circulated undetected in Brazil for more than a month as it spread north from Sã o Paulo. In Manaus, transmission reached unprecedented levels after a momentary respite in mid-2020. Faria et al. tracked the evolution of a new, more aggressive lineage called P.1, which has 17 mutations, including three (K417T, E484K, and N501Y) in the spike protein. After a period of accelerated evolution, this variant emerged in Brazil during November 2020. Coupled with the emergence of P.1, disease spread was accelerated by stark local inequalities and political upheaval, which compromised a prompt federal response. Science , abh1558 and abh2644, this issue p. 821 and p. 815

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Fig. 1 SARS-CoV-2 epidemiological, diagnostic, genomic, and mobility data from Manaus. (A) Dark solid line shows the 7-day rolling average of the COVID-19 confirmed and suspected daily time series of hospitalizations in Manaus. Admissions in Manaus are from Fundao de Vigilncia em Sade do Amazonas (66). Green dots indicate daily severe acute respiratory mortality records from the SIVEP-Gripe (Sistema de Informao de Vigilncia Epidemiolgica da Gripe) database (67). Red dots indicate excess burial records based on data from Manaus Mayors office for comparison (supplementary materials, materials and methods). The arrow indicates 6 December 2020, the date of the first P.1 case identified in Manaus by our study. (B) Maximum likelihood tree (n = 962 viral genomes) with B.1.1.28, P.1, and P.2 sequences, with collapsed views of P.1 and P.2 clusters and highlighting other sequences from Amazonas state, Brazil. Ancestral branches leading to P.1 and P.2 are shown as dashed lines. A more detailed phylogeny is available in fig. S3. Scale bar is shown in units of nucleotide substitutions per site (s/s). (C) Number of air travel passengers from Manaus to all states in Brazil was obtained from National Civil Aviation Agency of Brazil (www.gov.br/anac). The ISO 3166-2:BR codes of the states with genomic reports of P.1 [GISAID (68), as of 24 February 2021], are shown in bold. An updated list of GISAID genomes and reports of P.1 worldwide is available at https://cov-lineages.org/global_report_P.1.html. (D) Number of genome sequences from Manaus belonging to lineages of interest (supplementary materials, materials and methods). Spike mutations of interest are denoted.
Fig. 1 SARS-CoV-2 epidemiological, diagnostic, genomic, and mobility data from Manaus. (A) Dark solid line shows the 7-day rolling average of the COVID-19 confirmed and suspected daily time series of hospitalizations in Manaus. Admissions in Manaus are from Fundao de Vigilncia em Sade do Amazonas (66). Green dots indicate daily severe acute respiratory mortality records from the SIVEP-Gripe (Sistema de Informao de Vigilncia Epidemiolgica da Gripe) database (67). Red dots indicate excess burial records based on data from Manaus Mayors office for comparison (supplementary materials, materials and methods). The arrow indicates 6 December 2020, the date of the first P.1 case identified in Manaus by our study. (B) Maximum likelihood tree (n = 962 viral genomes) with B.1.1.28, P.1, and P.2 sequences, with collapsed views of P.1 and P.2 clusters and highlighting other sequences from Amazonas state, Brazil. Ancestral branches leading to P.1 and P.2 are shown as dashed lines. A more detailed phylogeny is available in fig. S3. Scale bar is shown in units of nucleotide substitutions per site (s/s). (C) Number of air travel passengers from Manaus to all states in Brazil was obtained from National Civil Aviation Agency of Brazil (www.gov.br/anac). The ISO 3166-2:BR codes of the states with genomic reports of P.1 [GISAID (68), as of 24 February 2021], are shown in bold. An updated list of GISAID genomes and reports of P.1 worldwide is available at https://cov-lineages.org/global_report_P.1.html. (D) Number of genome sequences from Manaus belonging to lineages of interest (supplementary materials, materials and methods). Spike mutations of interest are denoted.CC BY · PubMed Central
Fig. 2 Visualization of the time-calibrated maximum clade credibility tree reconstruction for B.1.1.28, P.1, and P.2 lineages in Brazil. Terminal branches and tips of Amazonas state are colored in brown, and those from other locations are colored in green (n = 962 viral genomes). Nodes with posterior probabilities of <0.5 have been collapsed into polytomies, and their range of divergence dates are illustrated as shaded expanses.
Fig. 2 Visualization of the time-calibrated maximum clade credibility tree reconstruction for B.1.1.28, P.1, and P.2 lineages in Brazil. Terminal branches and tips of Amazonas state are colored in brown, and those from other locations are colored in green (n = 962 viral genomes). Nodes with posterior probabilities of <0.5 have been collapsed into polytomies, and their range of divergence dates are illustrated as shaded expanses.CC BY · PubMed Central
Fig. 3 Temporal variation in the proportion of sequenced genomes belonging to P.1, and trends in quantitative RT-PCR Ct values for COVID-19 infections in Manaus. (A) Logistic function fitting to the proportion of genomes in sequenced infections that have been classified as P.1 (black circles, size indicating number of infections sequenced), divided up into time periods when the predicted proportion of infections that are due to P.1 is <1/3 (light brown), between 1/3 and 2/3 (green), and greater than 2/3 (gray). For the model fit, the darker ribbon indicates the 50% credible interval, and the lighter ribbon indicates the 95% credible interval. For the data points, the gray thick line is the 50% exact binomial CI, and the thinner line is the 95% exact binomial CI. (B) Ct values for genes E and N in a sample of symptomatic cases presenting for testing at a health care facility in Manaus (laboratory A), stratified according to the period defined in (A) in which the oropharyngeal and nasal swab collections occurred. (C) Ct values for genes E and N in a subsample of 184 infections included in (B) that had their genomes sequenced (dataset A).
Fig. 3 Temporal variation in the proportion of sequenced genomes belonging to P.1, and trends in quantitative RT-PCR Ct values for COVID-19 infections in Manaus. (A) Logistic function fitting to the proportion of genomes in sequenced infections that have been classified as P.1 (black circles, size indicating number of infections sequenced), divided up into time periods when the predicted proportion of infections that are due to P.1 is <1/3 (light brown), between 1/3 and 2/3 (green), and greater than 2/3 (gray). For the model fit, the darker ribbon indicates the 50% credible interval, and the lighter ribbon indicates the 95% credible interval. For the data points, the gray thick line is the 50% exact binomial CI, and the thinner line is the 95% exact binomial CI. (B) Ct values for genes E and N in a sample of symptomatic cases presenting for testing at a health care facility in Manaus (laboratory A), stratified according to the period defined in (A) in which the oropharyngeal and nasal swab collections occurred. (C) Ct values for genes E and N in a subsample of 184 infections included in (B) that had their genomes sequenced (dataset A).CC BY · PubMed Central
Fig. 4 Estimates of the epidemiological characteristics of P.1 inferred from a multicategory Bayesian transmission model fitted to data from Manaus, Brazil. (A) Joint posterior distribution of the cross-immunity and transmissibility increase inferred through fitting the model to mortality and genomic data. Gray contours indicate posterior density intervals ranging from the 95 and 50% isoclines. Marginal posterior distributions for each parameter shown along each axis. (B) As for (A), but showing the joint-posterior distribution of cross-immunity and the inferred relative risk of mortality in the period after emergence of P.1 compared with the period prior. (C) Daily incidence of COVID-19 mortality. Points indicate severe acute respiratory mortality records from the SIVEP-Gripe database (67, 69). Brown and green ribbons indicate model fit for COVID-19 mortality incidence, disaggregated by mortality attributable to non-P.1 lineages (brown) and the P.1 lineage (green). (D) Estimate of the proportion of P.1 infections through time in Manaus. Black data points with error bars are the empirical proportion observed in genomically sequenced cases (Fig. 3A), and green ribbons (dark = 50% BCI, light = 95% BCI) are the model fit to the data. (E) Estimated cumulative infection incidence for the P.1 and non-P.1 categories. Black data points with error bars are reversion-corrected estimates of seroprevalence from blood donors in Manaus (2). Colored ribbons are the model predictions of cumulative infection incidence for non-P.1 lineages (brown) and P.1 lineages (green). These points are shown for reference only and were not used to fit the model. (F) Bayesian posterior estimates of trends in reproduction number Rt for the P.1 and non-P.1 categories.
Fig. 4 Estimates of the epidemiological characteristics of P.1 inferred from a multicategory Bayesian transmission model fitted to data from Manaus, Brazil. (A) Joint posterior distribution of the cross-immunity and transmissibility increase inferred through fitting the model to mortality and genomic data. Gray contours indicate posterior density intervals ranging from the 95 and 50% isoclines. Marginal posterior distributions for each parameter shown along each axis. (B) As for (A), but showing the joint-posterior distribution of cross-immunity and the inferred relative risk of mortality in the period after emergence of P.1 compared with the period prior. (C) Daily incidence of COVID-19 mortality. Points indicate severe acute respiratory mortality records from the SIVEP-Gripe database (67, 69). Brown and green ribbons indicate model fit for COVID-19 mortality incidence, disaggregated by mortality attributable to non-P.1 lineages (brown) and the P.1 lineage (green). (D) Estimate of the proportion of P.1 infections through time in Manaus. Black data points with error bars are the empirical proportion observed in genomically sequenced cases (Fig. 3A), and green ribbons (dark = 50% BCI, light = 95% BCI) are the model fit to the data. (E) Estimated cumulative infection incidence for the P.1 and non-P.1 categories. Black data points with error bars are reversion-corrected estimates of seroprevalence from blood donors in Manaus (2). Colored ribbons are the model predictions of cumulative infection incidence for non-P.1 lineages (brown) and P.1 lineages (green). These points are shown for reference only and were not used to fit the model. (F) Bayesian posterior estimates of trends in reproduction number Rt for the P.1 and non-P.1 categories.CC BY · PubMed Central

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Authors
Faria, N. R., Mellan, T. A., Whittaker, C., Claro, I. M., Candido, D. D. S., Mishra, S., Crispim, M. A. E., Sales, F. C. S., Hawryluk, I., McCrone, J. T., Hulswit, R. J. G., Franco, L. A. M., Ramundo, M. S., de Jesus, J. G., Andrade, P. S., Coletti, T. M., Ferreira, G. M., Silva, C. A. M., Manuli, E. R., Pereira, R. H. M., Peixoto, P. S., Kraemer, M. U. G., Gaburo, N., Camilo, C. D. C., Hoeltgebaum, H., Souza, W. M., Rocha, E. C., de Souza, L. M., de Pinho, M. C., Araujo, L. J. T., Malta, F. S. V., de Lima, A. B., Silva, J. D. P., Zauli, D. A. G., Ferreira, A. C. D. S., Schnekenberg, R. P., Laydon, D. J., Walker, P. G. T., Schlüter, H. M., dos Santos, A. L. P., Vidal, M. S., Del Caro, V. S., Filho, R. M. F., dos Santos, H. M., Aguiar, R. S., Proença-Modena, J. L., Nelson, B., Hay, J. A., Monod, M., Miscouridou, X., Coupland, H., Sonabend, R., Vollmer, M., Gandy, A., Prete, C. A., Nascimento, V. H., Suchard, M. A., Bowden, T. A., Pond, S. L. K., Wu, C.-H., Ratmann, O., Ferguson, N. M., Dye, C., Loman, N. J., Lemey, P., Rambaut, A., Fraiji, N. A., Carvalho, M. D. P. S. S., Pybus, O. G., Flaxman, S., Bhatt, S., & Sabino, E. C.
Affiliations
  • Universidade de São Paulo (BR)
  • Imperial College London (GB)
  • Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil.
  • Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (BR)
  • University of Edinburgh (GB)
  • Centre for Human Genetics (GB)
  • Departamento de Epidemiologia, Faculdade de Saúde Pública da Universidade de São Paulo, Sao Paulo, Brazil.
  • Universidade Federal de Uberlândia (BR)
  • Institute of Applied Economic Research (BR)
  • University of Oxford (GB)
  • DB Diagnósticos do Brasil, São Paulo, Brazil.
  • Laboratory of Quantitative Pathology, Center of Pathology, Adolfo Lutz Institute, São Paulo, Brazil.
  • Instituto Hermes Pardini, Belo Horizonte, Brazil.
  • CDL Laboratório Santos e Vidal, Manaus, Brazil.
  • Universidade Federal de Minas Gerais (BR)
  • Laboratory of Emerging Viruses, Department of Genetics, Evolution, Microbiology, and Immunology, Institute of Biology, University of Campinas (UNICAMP), São Paulo, Brazil.
  • Instituto Nacional de Pesquisas da Amazônia (BR)
  • Harvard University (US)
  • Departamento de Engenharia de Sistemas Eletrônicos, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil.
  • University of California, Los Angeles (US)
  • Temple University (US)
  • University of Southampton (GB)
  • Institute for Microbiology and Infection, University of Birmingham, Birmingham, UK.
  • Rega Institute for Medical Research (BE)
  • Royal Veterinary College (GB)
  • University of Copenhagen (DK)
Corresponding author
  • Faria, Nuno R.
  • Mellan, Thomas A.
  • Whittaker, Charles
  • Claro, Ingra M.
  • Mishra, Swapnil
  • Pybus, Oliver G.
  • Flaxman, Seth
  • Bhatt, Samir
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2021-05-21
Volume
372
Issue
6544
Pages
815-821
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.abh2644
PMC
PMC8139423
OpenAlex
W3156343712
Cited by
1,461
References
93
Citations by year
2026: 48 · 2025: 73 · 2024: 104 · 2023: 227 · 2022: 492 · 2021: 502
FWCI
105.79
Type
journal article
Language
en
Open access
Licence
Locations
  • Science (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion, other-oa)
  • Spiral (Imperial College London) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • ePrints Soton (University of Southampton) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Lirias (KU Leuven) (submittedVersion, other-oa)
  • TUScholarShare (Temple University) (submittedVersion, cc-by)
Funders
  • Medical Research Council (MR/S0195/1)
  • National Institute for Health Research (203141/Z/16/Z, MR/S007555/1, ALTF 869-2019, OPP1175094, 2018/12579-7 CNP)
  • Wellcome Trust and Royal Society (204311/Z/16/Z)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
Keywords
P.1 lineage, SARS-CoV-2, COVID-19 pandemic, variants of concern, spike protein mutations, E484K, N501Y, ACE2 binding, transmissibility, immune evasion, molecular clock, epidemic resurgence, Manaus, genomic surveillance

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