Evolution and epidemic spread of SARS-CoV-2 in Brazil

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

Abstract

Brazil currently has one of the fastest-growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemics in the world. Because of limited available data, assessments of the impact of nonpharmaceutical interventions (NPIs) on this virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1 to 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 February and 11 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 traveled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil and provides evidence that current interventions remain insufficient to keep virus transmission under control in this country.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Figures

Fig. 1. SARS-CoV-2 epidemiology and epidemic spread…
Fig. 1. SARS-CoV-2 epidemiology and epidemic spread in Brazil.
(A) Cumulative number of SARS-CoV-2 reported cases (blue) and deaths (grey) in Brazil. (B) States are colored according to the number of cumulative confirmed cases by 30 April 2020. (C and D) Reproduction number (R) over time for the cities of São Paulo (C) and Rio de Janeiro (D). R were estimated using a Bayesian approach incorporating daily number of deaths and four variables related to mobility data (a social isolation index from Brazilian geolocation company InLoco, and Google mobility indices for time spent in transit stations, parks, and the average between groceries and pharmacies, retail and recreational, and workspaces). Dashed horizontal line indicates R = 1. Grey area and geometric symbols show the times at which NPIs interventions were implemented. Bayesian credible intervals (BCIs, 50 and 95%) are shown as shaded areas. The 2-letter ISO 3166-1 codes for the 27 federal units in Brazil are provided in Supplementary Information.
Fig. 2. Spatially-representative genomic sampling.
Fig. 2. Spatially-representative genomic sampling.
(A) Dumbbell plot showing the time intervals between date of collection of sampled genomes, notification of first cases and first deaths in each state. Red lines indicate the lag between the date of collection of first genome sequence and first reported case. The key for the 2-letter ISO 3166-1 codes for Brazilian federal units (or states) are provided in Supplementary Information. (B) Spearman’s rank (ρ) correlation between the number of SARI SARS-CoV-2 confirmed and SARI cases with unknown aetiology against number of sequences for each of the 21 Brazilian states included in this study (see also fig. S4). Circle sizes are proportional to the number of sequences for each federal unit. (C) Interval between the date of symptom onset and date of sample collection for the sequences generated in this study.
Fig. 3. Evolution and spread of SARS-CoV-2…
Fig. 3. Evolution and spread of SARS-CoV-2 in Brazil.
(A) Time-resolved maximum clade credibility phylogeny of 1182 SARS-CoV-2 sequences, 490 from Brazil (red) and 692 from outside Brazil (blue). The largest Brazilian clades are highlighted by grey boxes (Clade 1, Clade 2 and Clade 3). The panel A inset shows a root-to-tip regression of genetic divergence against dates of sample collection. (B) Dynamics of SARS-CoV-2 import events in Brazil. Dates of international and national (between federal states) migration events were estimated from virus genomes using a phylogeographic approach. The first phase was dominated by virus migrations from outside Brazil while the second phase is marked by virus spread within Brazil. Dashed vertical lines correspond to the mean posterior estimate for migration events from outside Brazil (blue) and within Brazil (red). (C) Locally estimated scatterplot smoothing of the daily number of international (blue) and national (red) air passengers in Brazil in 2020. T0 = date of first reported case in Brazil (25 February 2020).
Fig. 4. Spread of SARS-CoV-2 in Brazil.
Fig. 4. Spread of SARS-CoV-2 in Brazil.
(A) Spatiotemporal reconstruction of the spread of Brazilian SARS-CoV-2 clusters containing >2 sequences during the first (left) and the second epidemic phase (right) epidemic phase (Fig. 3B). Circles represent nodes of the MCC phylogeny and are colored according to their inferred time of occurrence. Shaded areas represent the 80% highest posterior density (HPD) interval and depict the uncertainty of the phylogeographic estimates for each node. Solid curved lines denote the links between nodes and the directionality of movement. Sequences belonging to clusters with <3 sequences were also plotted on the map with no lines connecting them. Background population density for each municipality was obtained from the Brazilian Institute of Geography (https://www.ibge.gov.br/). See fig. S14 for details of virus spread in the Southeast region. (B) Estimated number of within state (or within a given federal unit) and between-state (or between federal units) virus migrations over time. Dashed lines indicate estimates obtained during period of limited sampling (fig. S2). (C) Average distance in kilometres travelled by an air passenger per day in Brazil. Number of daily air passengers is shown in Fig. 3B. Light grey boxes indicate starting dates of NPIs across Brazil.

References

    1. Andersen K. G., Rambaut A., Lipkin W. I., Holmes E. C., Garry R. F., The proximal origin of SARS-CoV-2. Nat. Med. 26, 450–452 (2020). 10.1038/s41591-020-0820-9
    1. Wu F., Zhao S., Yu B., Chen Y.-M., Wang W., Song Z.-G., Hu Y., Tao Z.-W., Tian J.-H., Pei Y.-Y., Yuan M.-L., Zhang Y.-L., Dai F.-H., Liu Y., Wang Q.-M., Zheng J.-J., Xu L., Holmes E. C., Zhang Y.-Z., A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020). 10.1038/s41586-020-2008-3
    1. World Health Organization, Coronavirus Disease (COVID-2019) Situation Reports (2020); .
    1. Tian H., Liu Y., Li Y., Wu C.-H., Chen B., Kraemer M. U. G., Li B., Cai J., Xu B., Yang Q., Wang B., Yang P., Cui Y., Song Y., Zheng P., Wang Q., Bjornstad O. N., Yang R., Grenfell B. T., Pybus O. G., Dye C., An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 368, 638–642 (2020). 10.1126/science.abb6105
    1. Kraemer M. U. G., Yang C.-H., Gutierrez B., Wu C.-H., Klein B., Pigott D. M., du Plessis L., Faria N. R., Li R., Hanage W. P., Brownstein J. S., Layan M., Vespignani A., Tian H., Dye C., Pybus O. G., Scarpino S. V.; Open COVID-19 Data Working Group , The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497 (2020). 10.1126/science.abb4218
    1. Rambaut A., Holmes E. C., O’Toole Á., Hill V., McCrone J. T., Ruis C., du Plessis L., Pybus O. G., A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology. Nat. Microbiol. (2020). 10.1038/s41564-020-0770-5
    1. Russell T. W., Hellewell J., Jarvis C. I., van Zandvoort K., Abbott S., Ratnayake R., Flasche S., Eggo R. M., Edmunds W. J., Kucharski A. J.; Cmmid Covid-Working Group , Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020. Euro Surveill. 25, 2000256 (2020). 10.2807/1560-7917.ES.2020.25.12.2000256
    1. Verity R., Okell L. C., Dorigatti I., Winskill P., Whittaker C., Imai N., Cuomo-Dannenburg G., Thompson H., Walker P. G. T., Fu H., Dighe A., Griffin J. T., Baguelin M., Bhatia S., Boonyasiri A., Cori A., Cucunubá Z., FitzJohn R., Gaythorpe K., Green W., Hamlet A., Hinsley W., Laydon D., Nedjati-Gilani G., Riley S., van Elsland S., Volz E., Wang H., Wang Y., Xi X., Donnelly C. A., Ghani A. C., Ferguson N. M., Estimates of the severity of coronavirus disease 2019: A model-based analysis. Lancet Infect. Dis. 20, 669–677 (2020). 10.1016/S1473-3099(20)30243-7
    1. Wu J. T., Leung K., Bushman M., Kishore N., Niehus R., de Salazar P. M., Cowling B. J., Lipsitch M., Leung G. M., Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat. Med. 26, 506–510 (2020). 10.1038/s41591-020-0822-7
    1. Arons M. M., Hatfield K. M., Reddy S. C., Kimball A., James A., Jacobs J. R., Taylor J., Spicer K., Bardossy A. C., Oakley L. P., Tanwar S., Dyal J. W., Harney J., Chisty Z., Bell J. M., Methner M., Paul P., Carlson C. M., McLaughlin H. P., Thornburg N., Tong S., Tamin A., Tao Y., Uehara A., Harcourt J., Clark S., Brostrom-Smith C., Page L. C., Kay M., Lewis J., Montgomery P., Stone N. D., Clark T. A., Honein M. A., Duchin J. S., Jernigan J. A.; Public Health–Seattle and King County and CDC COVID-19 Investigation Team , Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N. Engl. J. Med. 382, 2081–2090 (2020). 10.1056/NEJMoa2008457
    1. Ferretti L., Wymant C., Kendall M., Zhao L., Nurtay A., Abeler-Dörner L., Parker M., Bonsall D., Fraser C., Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936 (2020). 10.1126/science.abb6936
    1. Lavezzo E., Franchin E., Ciavarella C., Cuomo-Dannenburg G., Barzon L., Del Vecchio C., Rossi L., Manganelli R., Loregian A., Navarin N., Abate D., Sciro M., Merigliano S., De Canale E., Vanuzzo M. C., Besutti V., Saluzzo F., Onelia F., Pacenti M., Parisi S., Carretta G., Donato D., Flor L., Cocchio S., Masi G., Sperduti A., Cattarino L., Salvador R., Nicoletti M., Caldart F., Castelli G., Nieddu E., Labella B., Fava L., Drigo M., Gaythorpe K. A. M., Brazzale A. R., Toppo S., Trevisan M., Baldo V., Donnelly C. A., Ferguson N. M., Dorigatti I., Crisanti A., Suppression of COVID-19 outbreak in the municipality of Vo, Italy. Nature (2020). 10.1038/s41586-020-2488-1
    1. Mizumoto K., Kagaya K., Zarebski A., Chowell G., Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill. 25, 2000180 (2020). 10.2807/1560-7917.ES.2020.25.10.2000180
    1. Brazilian Ministry of Health, Painel de Casos de Doença Pelo Coronavírus 2019 (COVID-19) No Brasil Pelo Ministério da Saúde (2020); .
    1. W. M. de Souza, L. Fletcher Buss, D. da Silva Candido, J. P. Carrera, S. Li, A. Zarebski, M. Vincenti-Gonzalez, J. Messina, F. C. da Silva Sales, P. dos Santos Andrade, C. A. Prete Jr., V. H. Nascimento, F. Ghilardi, R. H. Moraes Pereira, A. A. de Souza Santos, L. Abade, B. Gutierrez, M. U. G. Kraemer, R. Santana Aguiar, N. Alexander, P. Mayaud, O. J. Brady, I. O. M. de Souza, N. Gouveia, G. Li, A. Tami, S. Barbosa Oliveira, V. B. Gomes Porto, F. Ganem, W. Ferreira Almeida, F. Fontana Sutile Tardetti Fantinato, E. Marques Macario, W. Kleber Oliveira, O. Pybus, C.-H. Wu, J. Croda, E. Cerdeira Sabino, N. R. Faria, Epidemiological and clinical characteristics of the early phase of the COVID-19 epidemic in Brazil. medRxiv 10.1101/2020.04.25.20077396 [Preprint]. 29 April 2020; .10.1101/2020.04.25.20077396
    1. Croda J., Oliveira W. K., Frutuoso R. L., Mandetta L. H., Baia-da-Silva D. C., Brito-Sousa J. D., Monteiro W. M., Lacerda M. V. G., COVID-19 in Brazil: Advantages of a socialized unified health system and preparation to contain cases. Rev. Soc. Bras. Med. Trop. 53, e20200167 (2020). 10.1590/0037-8682-0167-2020
    1. Croda J., Garcia L., Immediate health surveillance response to COVID-19 epidemic [in Portuguese]. Epidemiol. Ser. Saúde 29, e2020002 (2020). 10.5123/S1679-49742020000100021
    1. S. B. Oliveira, V. Bertollo Gomes Porto, F. Ganem, F. Macedo Mendes, M. Almiron, W. Kleber de Oliveira, F. Fontana Sutile Tardetti Fantinato, W. Aparecida Ferreira de Almeida, A. Pereira de Macedo Borges, H. Natan Batista Pinheiro, R. dos Santos Oliveira, J. R. Andrews, N. R. Faria, M. Barreto Lopes, W. Araujo, F. A. Diaz-Quijano, H. I. Nakaya, J. Croda, Monitoring social distancing and SARS-CoV-2 transmission in Brazil using cell phone mobility data. medRxiv 2020.04.30.20082172 [Preprint] (5 May 2020); .10.1101/2020.04.30.20082172
    1. S. M. Kissler, Reductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York City (Harvard DASH Repository, 2020); .
    1. H. J. T. Unwin, S. Mishra, V. C. Bradley, A. Gandy, M. Vollmer, T. Mellan, H. Coupland, K. Ainslie, C. Whittaker, J. Ish-Horowicz, S. Filippi, X. Xi, M. Monod, O. Ratmann, M. Hutchinson, F. Valka, H. Zhu, I. Hawryluk, P. Milton, M. Baguelin, A. Boonyasiri, N. Brazeau, L. Cattarino, G. Charles, L. V. Cooper, Z. Cucunuba, G. Cuomo-Dannenburg, B. Djaafara, I. Dorigatti, O. J. Eales, J. Eaton, S. van Elsland, R. FitzJohn, K. Gaythorpe, W. Green, T. Hallett, W. Hinsley, N. Imai, B. Jeffrey, E. Knock, D. Laydon, J. Lees, G. Nedjati-Gilani, P. Nouvellet, L. Okell, A. Ower, K. V. Parag, I. Siveroni, H. A. Thompson, R. Verity, P. Walker, C. Walters, Y. Wang, O. J. Watson, L. Whittles, A. Ghani, N. M. Ferguson, S. Riley, C. A. Donnelly, S. Bhatt, S. Flaxman, Report 23: State-Level Tracking of COVID-19 in the United States (21-05-2020) (Imperial College London, 2020); .10.25561/79231
    1. Flaxman S., Mishra S., Gandy A., Unwin H. J. T., Mellan T. A., Coupland H., Whittaker C., Zhu H., Berah T., Eaton J. W., Monod M., Ghani A. C., Donnelly C. A., Riley S., Vollmer M. A. C., Ferguson N. M., Okell L. C., Bhatt S.; Imperial College COVID-19 Response Team , Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature (2020). 10.1038/s41586-020-2405-7
    1. T. A. Mellan, H. H. Hoeltgebaum, S. Mishra, C. Whittaker, R. P. Schnekenberg, A. Gandy, H. J. T. Unwin, M. A. C. Vollmer, H. Coupland, I. Hawryluk, N. Rodrigues Faria, J. Vesga, H. Zhu, M. Hutchinson, O. Ratmann, M. Monod, K. Ainslie, M. Baguelin, S. Bhatia, A. Boonyasiri, N. Brazeau, G. Charles, L. V. Cooper, Z. Cucunuba, G. Cuomo-Dannenburg, A. Dighe, B. Djaafara, J. Eaton, S. L. van Elsland, R. FitzJohn, K. Fraser, K. Gaythorpe, W. Green, S. Hayes, N. Imai, B. Jeffrey, E. Knock, D. Laydon, J. Lees, T. Mangal, A. Mousa, G. Nedjati-Gilani, P. Nouvellet, D. Olivera, K. V. Parag, M. Pickles, H. A. Thompson, R. Verity, C. Walters, H. Wang, Y. Wang, O. J. Watson, L. Whittles, X. Xi, L. Okell, I. Dorigatti, P. Walker, A. Ghani, S. Riley, N. M. Ferguson, C. A. Donnelly, S. Flaxman, S. Bhatt, Report 21: Estimating COVID-19 Cases and Reproduction Number in Brazil (2020); .10.25561/78872
    1. Y.-Z. Zhang, E. C. Holmes, Novel 2019 coronavirus genome, Virological (2020); .
    1. Corman V. M., Landt O., Kaiser M., Molenkamp R., Meijer A., Chu D. K. W., Bleicker T., Brünink S., Schneider J., Schmidt M. L., Mulders D. G. J. C., Haagmans B. L., van der Veer B., van den Brink S., Wijsman L., Goderski G., Romette J.-L., Ellis J., Zambon M., Peiris M., Goossens H., Reusken C., Koopmans M. P. G., Drosten C., Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro Surveill. 25,2000045 (2020). 10.2807/1560-7917.ES.2020.25.3.2000045
    1. Thi Nhu Thao T., Labroussaa F., Ebert N., V’kovski P., Stalder H., Portmann J., Kelly J., Steiner S., Holwerda M., Kratzel A., Gultom M., Schmied K., Laloli L., Hüsser L., Wider M., Pfaender S., Hirt D., Cippà V., Crespo-Pomar S., Schröder S., Muth D., Niemeyer D., Corman V. M., Müller M. A., Drosten C., Dijkman R., Jores J., Thiel V., Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform. Nature 582, 561–565 (2020). 10.1038/s41586-020-2294-9
    1. P. C. Resende, E. Delatorre, T. Gräf, D. Mir, F. do Couto Motta, L. Reis Appolinario, A. C. Dias da Paixão, M. Ogrzewalska, B. Caetano, M. Cordeiro dos Santos, J. de Almeida Ferreira, E. Costa Santos Junior, S. Patroca da Silva, S. Bianchini Fernandes, L. A. Vianna, L. da Costa Souza, J. F. G. Ferro, V. B. Nardy, J. Croda, W. K. Oliveira, A. Abreu, G. Bello, M. M. Siqueira, Genomic surveillance of SARS-CoV-2 reveals community transmission of a major lineage during the early pandemic phase in Brazil. bioRxiv 020.06.17.158006 [Preprint] (2020); .10.1101/2020.06.17.158006
    1. J. Xavier, M. Giovanetti, T. Adelino, V. Fonseca, A. V. Barbosa da Costa, A. Aparecida Ribeiro, K. Nascimento Felicio, C. Guerra Duarte, M. V. Ferreira Silva, A. Salgado, M. Teixeira Lima, R. de Jesus, A. Fabri, C. Franco Soares Zoboli, T. Gutemberg Souza Santos, F. Iani, A. M. Bispo de Filippis, M. Agudo Mendonca Teixeira de Siqueira, A. L. de Abreu, V. de Azevedo, D. Brock Ramalho, C. F. Campelo de Albuquerque, T. de Oliveira, E. C. Holmes, J. Lourenco, L. C. Junior Alcantara, M. Aparecida Assuncao Oliveira, The ongoing COVID-19 epidemic in Minas Gerais, Brazil: insights from epidemiological data and SARS-CoV-2 whole genome sequencing. medRxiv 2020.2005.2005.20091611 [Preprint] (2020); .10.1101/2020.05.05.20091611
    1. Nascimento V. A., Corado A. L. G., Nascimento F. O., Costa A. K. A., Duarte D. C. G., Jesus M. S., Luz S. L. B., Goncalves L. M. F., Costa C. F., Delatorre E., Naveca F. G., Genomic and phylogenetic characterization of an imported case of SARS-CoV-2 in Amazonas State, Brazil. Memoirs of the Oswaldo Cruz Institute 10.1590/0074-02760200310 (2020).
    1. Shu Y., McCauley J., GISAID: Global initiative on sharing all influenza data – from vision to reality. Euro. Surveill. 22, 30494 (2017) 10.1016/S0140-6736(13)61887-5
    1. Cotten M., Watson S. J., Kellam P., Al-Rabeeah A. A., Makhdoom H. Q., Assiri A., Al-Tawfiq J. A., Alhakeem R. F., Madani H., AlRabiah F. A., Al Hajjar S., Al-nassir W. N., Albarrak A., Flemban H., Balkhy H. H., Alsubaie S., Palser A. L., Gall A., Bashford-Rogers R., Rambaut A., Zumla A. I., Memish Z. A., Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: A descriptive genomic study. Lancet 382, 1993–2002 (2013). 10.1016/S0140-6736(13)61887-5
    1. Cotten M., Watson S. J., Zumla A. I., Makhdoom H. Q., Palser A. L., Ong S. H., Al Rabeeah A. A., Alhakeem R. F., Assiri A., Al-Tawfiq J. A., Albarrak A., Barry M., Shibl A., Alrabiah F. A., Hajjar S., Balkhy H. H., Flemban H., Rambaut A., Kellam P., Memish Z. A., Spread, circulation, and evolution of the Middle East respiratory syndrome coronavirus. mBio 5, e01062-13 (2014). 10.1128/mBio.01062-13
    1. Dudas G., Carvalho L. M., Rambaut A., Bedford T., MERS-CoV spillover at the camel-human interface. eLife 7, e31257 (2018). 10.7554/eLife.31257
    1. Zhao Z., Li H., Wu X., Zhong Y., Zhang K., Zhang Y.-P., Boerwinkle E., Fu Y.-X., Moderate mutation rate in the SARS coronavirus genome and its implications. BMC Evol. Biol. 4, 21 (2004). 10.1186/1471-2148-4-21
    1. S. Duchene, L. Featherstone, M. Haritopoulou-Sinanidou, A. Rambaut, P. Lemey, G. Baele, Temporal signal and the phylodynamic threshold of SARS-CoV-2. bioRxiv 2020.05.04.077735 [Preprint] (2020); .10.1101/2020.05.04.077735
    1. Lu J., du Plessis L., Liu Z., Hill V., Kang M., Lin H., Sun J., François S., Kraemer M. U. G., Faria N. R., McCrone J. T., Peng J., Xiong Q., Yuan R., Zeng L., Zhou P., Liang C., Yi L., Liu J., Xiao J., Hu J., Liu T., Ma W., Li W., Su J., Zheng H., Peng B., Fang S., Su W., Li K., Sun R., Bai R., Tang X., Liang M., Quick J., Song T., Rambaut A., Loman N., Raghwani J., Pybus O. G., Ke C., Genomic Epidemiology of SARS-CoV-2 in Guangdong Province, China. Cell 181, 997–1003.e9 (2020). 10.1016/j.cell.2020.04.023
    1. Candido D. D. S., Watts A., Abade L., Kraemer M. U. G., Pybus O. G., Croda J., de Oliveira W., Khan K., Sabino E. C., Faria N. R., Routes for COVID-19 importation in Brazil. J. Travel Med. 27, taaa042 (2020). 10.1093/jtm/taaa042
    1. S. Dellicour, K. Durkin, S. L. Hong, B. Vanmechelen, J. Martí-Carreras, M. S. Gill, C. Meex, S. Bontems, E. André, M. Gilbert, C. Walker, N. De Maio, N. R. Faria, J. Hadfield, M.-P. Hayette, V. Bours, T. Wawina-Bokalanga, M. Artesi, G. Baele, P. Maes, A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS-CoV-2 lineages. bioRxiv 2020.05.05.078758 [Preprint] (2020); .10.1101/2020.05.05.078758
    1. World Health Organization, Coronavirus disease 2019 (COVID-19): Situation report –72 (WHO, 2020); .
    1. Centre for Genomic Pathogen Surveillance, Imperial College London, Report of 427 novel genomes from Brazil and the associated metadata, Microreact (2020); .
    1. Data and code for: D. S. Candido, I. M. Claro, J. G. de Jesus, W. M. Souza, F. R. R. Moreira, S. Dellicour, T. A. Mellan, L. du Plessis, R. H. M. Pereira, F. C. S. Sales, E. R. Manuli, J. Thézé, L. Almeida, M. T. Menezes, C. M. Voloch, M. J. Fumagalli, T. M. Coletti, C. A. M. da Silva, M. S. Ramundo, M. R. Amorim, H. Hoeltgebaum, S. Mishra, M. S. Gill, L. M. Carvalho, L. F. Buss, C. A. Prete Jr., J. Ashworth, H. I. Nakaya, P. S. Peixoto, O. J. Brady, S. M. Nicholls, A. Tanuri, Á. D. Rossi, C. K. V. Braga, A. L. Gerber, A. P. de C. Guimarães, N. Gaburo Jr., C. Salete Alencar, A. C. S. Ferreira, C. X. Lima, J. E. Levi, C. Granato, G. M. Ferreira, R. S. Francisco Jr., F. Granja, M. T. Garcia, M. L. Moretti, M. W. Perroud Jr., T. M. P. P. Castiñeiras, C. S. Lazari, S. C. Hill, A. A. de Souza Santos, C. L. Simeoni, J. Forato, A. C. Sposito, A. Z. Schreiber, M. N. N. Santos, C. Zolini de Sá, R. P. Souza, L. C. Resende-Moreira, M. M. Teixeira, J. Hubner, P. A. F. Leme, R. G. Moreira, M. L. Nogueira, CADDE-Genomic-Network, N. M. Ferguson, S. F. Costa, J. L. Proenca-Modena, A. T. R. Vasconcelos, S. Bhatt, P. Lemey, C.-H. Wu, A. Rambaut, N. J. Loman, R. S. Aguiar, O. G. Pybus, E. C. Sabino, N. Rodrigues Faria, Evolution and epidemic spread of SARS-CoV-2 in Brazil, Dryad (2020); .10.5061/dryad.rxwdbrv5z
    1. A. Aktay, S. Bavadekar, G. Cossoul, J. Davis, D. Desfontaines, A. Fabrikant, E. Gabrilovich, K. Gadepalli, B. Gipson, M. Guevara, C. Kamath, M. Kansal, A. Lange, C. Mandayam, A. Oplinger, C. Pluntke, T. Roessler, A. Schlosberg, T. Shekel, S. Vispute, M. Vu, G. Wellenius, B. Williams, R. J. Wilson, Google COVID-19 community mobility reports: Anonymization process description (version 1.0). [] (8 April 2020).
    1. inloco, Mapa Brasileiro da COVID-19 (2020); .
    1. Peixoto P. S., Marcondes D., Peixoto C., Oliva S. M., Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil. PLoS One 15, e0235732 (2020). 10.1093/jtm/taaa021
    1. Liu Y., Gayle A. A., Wilder-Smith A., Rocklöv J., The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med. 27, taaa021 (2020). 10.1093/jtm/taaa021
    1. Waggoner J. J., Stittleburg V., Pond R., Saklawi Y., Sahoo M. K., Babiker A., Hussaini L., Kraft C. S., Pinsky B. A., Anderson E. J., Rouphael N., Triplex real-time RT-PCR for severe acute respiratory syndrome coronavirus 2. Emerg. Infect. Dis. 26, 1633–1635 (2020). 10.3201/eid2607.201285
    1. Quick J., Grubaugh N. D., Pullan S. T., Claro I. M., Smith A. D., Gangavarapu K., Oliveira G., Robles-Sikisaka R., Rogers T. F., Beutler N. A., Burton D. R., Lewis-Ximenez L. L., de Jesus J. G., Giovanetti M., Hill S. C., Black A., Bedford T., Carroll M. W., Nunes M., Alcantara Jr L. C., Sabino E. C., Baylis S. A., Faria N. R., Loose M., Simpson J. T., Pybus O. G., Andersen K. G., Loman N. J., Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. Nat. Protoc. 12, 1261–1276 (2017). 10.1038/nprot.2017.066
    1. Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R.; 1000 Genome Project Data Processing Subgroup , The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). 10.1093/bioinformatics/btp352
    1. Milne I., Bayer M., Cardle L., Shaw P., Stephen G., Wright F., Marshall D., Tablet: Next generation sequence assembly visualization. Bioinformatics 26, 401–402 (2010). 10.1093/bioinformatics/btp666
    1. Katoh K., Standley D. M., MAFFT: Iterative refinement and additional methods. Methods Mol. Biol. 1079, 131–146 (2014). 10.1007/978-1-62703-646-7_8
    1. Hasegawa M., Kishino H., Yano T., Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22, 160–174 (1985). 10.1007/BF02101694
    1. Yang Z., Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: Approximate methods. J. Mol. Evol. 39, 306–314 (1994). 10.1007/BF00160154
    1. Minh B. Q., Schmidt H. A., Chernomor O., Schrempf D., Woodhams M. D., von Haeseler A., Lanfear R., IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol. Biol. Evol. 37, 1530–1534 (2020). 10.1093/molbev/msaa015
    1. Rambaut A., Lam T. T., Max Carvalho L., Pybus O. G., Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2, vew007 (2016). 10.1093/ve/vew007
    1. Singer J., Gifford R., Cotten M., Robertson D., CoV-GLUE: A web application for tracking SARS-CoV-2 genomic variation (2020); .10.20944/preprints202006.0225.v1
    1. Jesus J. G., Sacchi C., Candido D. D. S., Claro I. M., Sales F. C. S., Manuli E. R., Silva D. B. B. D., Paiva T. M., Pinho M. A. B., Santos K. C. O., Hill S. C., Aguiar R. S., Romero F., Santos F. C. P. D., Gonçalves C. R., Timenetsky M. D. C., Quick J., Croda J. H. R., Oliveira W., Rambaut A., Pybus O. G., Loman N. J., Sabino E. C., Faria N. R., Importation and early local transmission of COVID-19 in Brazil, 2020. Rev. Inst. Med. Trop. São Paulo 62, e30 (2020). 10.1590/s1678-9946202062030
    1. Centers for Disease Control and Prevention, Research Use Only 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Primers and Probes (2020); .
    1. J. Northill, I. Mackay, Wuhan coronavirus (2019-nCoV) real-time RT-PCR N gene 2020 (Wuhan-N) V.1 (2020); .
    1. N. Nao, Shirato, K., Katano, H., Matsuyama, S., Takeda, M., Detection of second case of 2019-nCoV infection in Japan (corrected version) (2020); .
    1. Thailand Ministry of Public Health, Diagnostic detection of novel coronavirus 2019 by real time RT-PCR (2020); .
    1. Chinese National Institute for Viral Disease Control and Prevention, Specific primers and probes for detection 2019 novel coronavirus (2020); .
    1. HKU Med, LKS Faculty of Medicine, School of Public Health, Detection of, 2019 novel coronavirus (2019-nCoV) in suspected human cases by RT-PCR (2020); .
    1. Bruen T. C., Philippe H., Bryant D., A simple and robust statistical test for detecting the presence of recombination. Genetics 172, 2665–2681 (2006). 10.1534/genetics.105.048975
    1. Huson D. H., Bryant D., Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006). 10.1093/molbev/msj030
    1. Martin D. P., Murrell B., Golden M., Khoosal A., Muhire B., RDP4: Detection and analysis of recombination patterns in virus genomes. Virus Evol. 1, vev003 (2015). 10.1093/ve/vev003
    1. Suchard M. A., Lemey P., Baele G., Ayres D. L., Drummond A. J., Rambaut A., Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018). 10.1093/ve/vey016
    1. Ayres D. L., Darling A., Zwickl D. J., Beerli P., Holder M. T., Lewis P. O., Huelsenbeck J. P., Ronquist F., Swofford D. L., Cummings M. P., Rambaut A., Suchard M. A., BEAGLE: An application programming interface and high-performance computing library for statistical phylogenetics. Syst. Biol. 61, 170–173 (2012). 10.1093/sysbio/syr100
    1. Gill M. S., Lemey P., Faria N. R., Rambaut A., Shapiro B., Suchard M. A., Improving Bayesian population dynamics inference: A coalescent-based model for multiple loci. Mol. Biol. Evol. 30, 713–724 (2013). 10.1093/molbev/mss265
    1. Ferreira M. A. R., Suchard M. A., Bayesian analysis of elapsed times in continuous-time Markov chains. Can. J. Stat. 36, 355–368 (2008). 10.1002/cjs.5550360302
    1. Rambaut A., Drummond A. J., Xie D., Baele G., Suchard M. A., Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst. Biol. 67, 901–904 (2018). 10.1093/sysbio/syy032
    1. Lemey P., Rambaut A., Drummond A. J., Suchard M. A., Bayesian phylogeography finds its roots. PLOS Comput. Biol. 5, e1000520 (2009). 10.1371/journal.pcbi.1000520
    1. Faria N. R., Rambaut A., Suchard M. A., Baele G., Bedford T., Ward M. J., Tatem A. J., Sousa J. D., Arinaminpathy N., Pépin J., Posada D., Peeters M., Pybus O. G., Lemey P., HIV epidemiology. The early spread and epidemic ignition of HIV-1 in human populations. Science 346, 56–61 (2014). 10.1126/science.1256739
    1. O’Brien J. D., Minin V. N., Suchard M. A., Learning to count: Robust estimates for labeled distances between molecular sequences. Mol. Biol. Evol. 26, 801–814 (2009). 10.1093/molbev/msp003
    1. Minin V. N., Suchard M. A., Fast, accurate and simulation-free stochastic mapping. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 3985–3995 (2008). 10.1098/rstb.2008.0176
    1. Minin V. N., Suchard M. A., Counting labeled transitions in continuous-time Markov models of evolution. J. Math. Biol. 56, 391–412 (2008). 10.1007/s00285-007-0120-8
    1. Lemey P., Rambaut A., Welch J. J., Suchard M. A., Phylogeography takes a relaxed random walk in continuous space and time. Mol. Biol. Evol. 27, 1877–1885 (2010). 10.1093/molbev/msq067
    1. Dellicour S., Baele G., Dudas G., Faria N. R., Pybus O. G., Suchard M. A., Rambaut A., Lemey P., Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. Nat. Commun. 9, 2222 (2018). 10.1038/s41467-018-03763-2
    1. Dellicour S., Rose R., Faria N. R., Lemey P., Pybus O. G., SERAPHIM: Studying environmental rasters and phylogenetically informed movements. Bioinformatics 32, 3204–3206 (2016). 10.1093/bioinformatics/btw384

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi