The University of Southampton
University of Southampton Institutional Repository

Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil

Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil
Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil
Please see Materials and Methods section in Supplementary Materials.,Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within-state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country.,This REPOSITORY contains the data used for the main figures of the manuscript. Data for main figures: Figure 1 Fig1a: Cumulative number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1b: number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1c: reproduction number (Rt) estimates and confidence intervals for São Paulo city. Fig1d: reproduction number (Rt) estimates and confidence intervals for Rio de Janeiro city. Figue 2 Fig2a: Dates of first confirmed Covid-19 case and death for each Brazilian state. Dates and states of collection of all 427 sequences generated in this study. Fig2b: Number of SARI Covid-19 confirmed cases and number of novel genomes generated in this study per Brazilian state. Fig3c: Number of samples per lag time (in days) between symptom onset and collection date. Figure 3 Fig3a: Maximum Clade Credibility (MCC) tree of 1,182 SARS-CoV-2 genomes, including 427 novel genomes for Brazil. For details, see supplementary data. Fig3c: Number of national and international air passengers in Brazil per flight and per day from January to 30th April 2020. Figue 4 Fig4a: Continuous phylogeography Fig4b: Discrete phylogeography Fig4c: Average distance of air travel in Brazil
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Wu, Chieh-Hsi
ace630c6-2095-4ade-b657-241692f6b4d3
Rambaut, Andrew
2b7f87ef-95c8-4069-a3db-bca4b38271ec
Loman, Nick J.
dbb36efc-ad81-4775-9f11-baf03a103529
Aguiar, Renato S.
660aa490-d334-40cd-a5c7-21fbeb9326da
Pybus, Oliver G.
5fa128e1-8eb8-4d38-b925-1d7869a07f99
Sabino, Ester C.
36d8182b-1c56-4071-abd6-33bdd931fdb7
Faria, Nuno R.
cc78a30a-2b32-4b9b-9d46-5c7bd842da37

(2020) Data from: Evolution and epidemic spread of SARS-CoV-2 in Brazil. DRYAD doi:10.5061/dryad.rxwdbrv5z [Dataset]

Record type: Dataset

Abstract

Please see Materials and Methods section in Supplementary Materials.,Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within-state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country.,This REPOSITORY contains the data used for the main figures of the manuscript. Data for main figures: Figure 1 Fig1a: Cumulative number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1b: number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1c: reproduction number (Rt) estimates and confidence intervals for São Paulo city. Fig1d: reproduction number (Rt) estimates and confidence intervals for Rio de Janeiro city. Figue 2 Fig2a: Dates of first confirmed Covid-19 case and death for each Brazilian state. Dates and states of collection of all 427 sequences generated in this study. Fig2b: Number of SARI Covid-19 confirmed cases and number of novel genomes generated in this study per Brazilian state. Fig3c: Number of samples per lag time (in days) between symptom onset and collection date. Figure 3 Fig3a: Maximum Clade Credibility (MCC) tree of 1,182 SARS-CoV-2 genomes, including 427 novel genomes for Brazil. For details, see supplementary data. Fig3c: Number of national and international air passengers in Brazil per flight and per day from January to 30th April 2020. Figue 4 Fig4a: Continuous phylogeography Fig4b: Discrete phylogeography Fig4c: Average distance of air travel in Brazil

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Published date: 2020

Identifiers

Local EPrints ID: 449439
URI: http://eprints.soton.ac.uk/id/eprint/449439
PURE UUID: b7dc992c-67d1-40b9-bebc-45cd59344caf
ORCID for Chieh-Hsi Wu: ORCID iD orcid.org/0000-0001-9386-725X

Catalogue record

Date deposited: 28 May 2021 16:32
Last modified: 02 Dec 2022 03:00

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Contributors

Contributor: Darlan S. Candido
Contributor: Ingra M. Claro
Contributor: Jaqueline G. De Jesus
Contributor: William M. Souza
Contributor: Filipe R. R. Moreira
Contributor: Simon Dellicour
Contributor: Thomas A. Mellan
Contributor: Louis Du Plessis
Contributor: Rafael H. M. Pereira
Contributor: Flavia C. S. Sales
Contributor: Erika R. Manuli
Contributor: Julien Thézé
Contributor: Luiz Almeida
Contributor: Mariane T. Menezes
Contributor: Carolina M. Voloch
Contributor: Marcilio J. Fumagalli
Contributor: Thaís M. Coletti
Contributor: Camila A. M. Da Silva
Contributor: Mariana S. Ramundo
Contributor: Mariene R. Amorim
Contributor: Henrique H. Hoeltgebaum
Contributor: Swapnil Mishra
Contributor: Mandev S. Gill
Contributor: Luiz M. Carvalho
Contributor: Lewis F. Buss
Contributor: Carlos A. Prete
Contributor: Jordan Ashworth
Contributor: Helder I. Nakaya
Contributor: Pedro S. Peixoto
Contributor: Oliver J. Brady
Contributor: Samuel M. Nicholls
Contributor: Amilcar Tanuri
Contributor: Átila D. Rossi
Contributor: Carlos K.V. Braga
Contributor: Alexandra L. Gerber
Contributor: Ana Paula De C. Guimarães
Contributor: Nelson Gaburo
Contributor: Cecila Salete Alencar
Contributor: Alessandro C.S. Ferreira
Contributor: Cristiano X. Lima
Contributor: José Eduardo Levi
Contributor: Celso Granato
Contributor: Giulia M. Ferreira
Contributor: Ronaldo S. Francisco
Contributor: Fabiana Granja
Contributor: Marcia T. Garcia
Contributor: Maria Luiza Moretti
Contributor: Amilcar Tanuri
Contributor: Mauricio W. Perroud
Contributor: Átila D. Rossi
Contributor: Carlos K.V. Braga
Contributor: Alexandra L. Gerber
Contributor: Terezinha M. P. P. Castiñeiras
Contributor: Ana Paula De C. Guimarães
Contributor: Nelson Gaburo
Contributor: Carolina S. Lazari
Contributor: Cecila Salete Alencar
Contributor: Alessandro C.S. Ferreira
Contributor: Cristiano X. Lima
Contributor: Sarah C. Hill
Contributor: José Eduardo Levi
Contributor: Andreza Aruska De Souza Santos
Contributor: Celso Granato
Contributor: Giulia M. Ferreira
Contributor: Ronaldo S. Francisco
Contributor: Camila L. Simeoni
Contributor: Fabiana Granja
Contributor: Julia Forato
Contributor: Marcia T. Garcia
Contributor: Andrei C. Sposito
Contributor: Maria Luiza Moretti
Contributor: Mauricio W. Perroud
Contributor: Angelica Z. Schreiber
Contributor: Terezinha M. P. P. Castiñeiras
Contributor: Carolina S. Lazari
Contributor: Sarah C. Hill
Contributor: Magnun N. N. Santos
Contributor: Andreza Aruska De Souza Santos
Contributor: Camila Zolini De Sá
Contributor: Camila L. Simeoni
Contributor: Julia Forato
Contributor: Andrei C. Sposito
Contributor: Renan P. Souza
Contributor: Angelica Z. Schreiber
Contributor: Luciana C. Resende-Moreira
Contributor: Magnun N. N. Santos
Contributor: Camila Zolini De Sá
Contributor: Renan P. Souza
Contributor: Luciana C. Resende-Moreira
Contributor: Mauro M. Teixeira
Contributor: Josy Hubner
Contributor: Patricia A. F. Leme
Contributor: Rennan G Moreira
Contributor: Maurício L. Nogueira
Contributor: Neil M Ferguson
Contributor: Silvia F. Costa
Contributor: José Luiz Proenca-Modena
Contributor: Ana Tereza R. Vasconcelos
Contributor: Samir Bhatt
Contributor: Philippe Lemey
Contributor: Chieh-Hsi Wu ORCID iD
Contributor: Andrew Rambaut
Contributor: Nick J. Loman
Contributor: Renato S. Aguiar
Contributor: Oliver G. Pybus
Contributor: Ester C. Sabino
Contributor: Nuno R. Faria

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