MedRxiv
Supplementary Materials forEvolution and epidemic spread of SARS-CoV-2 in BrazilAuthors: Darlan S. Candido1,?, Ingra M. Claro2,?, Jaqueline G. de Jesus2,?, William M. Souza3,?, Filipe R. R. Moreira4,?, Simon Dellicour5,6?, Thomas A. Mellan7,?, Louis du Plessis1, Rafael H. M. Pereira8, Flavia C. S. Sales2, Erika R. Manuli2, Julien Thézé9, Luiz Almeida10, Mariane T. Menezes4, Carolina M. Voloch4, Marcilio J. Fumagalli3, Thais M. Coletti2, Camila A. M. Silva2, Mariana S. Ramundo2, Mariene R. Amorim11, Henrique Hoeltgebaum12, Swapnil Mishra6, Mandev S. Gill6, Luiz M. Carvalho13, Lewis F. Buss2, Carlos A. Prete Jr14, Jordan Ashworth15, Helder Nakaya16, Pedro S. Peixoto17, Oliver J. Brady18,19, Samuel M. Nicholls20, Amilcar Tanuri4, ?tila D. Rossi4, Carlos K.V. Braga8, Alexandra L. Gerber10, Ana Paula Guimar?es10, Nelson Gaburo Jr21, Cecila S. Alencar22, Alessandro C.S. Ferreira23, Cristiano X. Lima24,25, José Eduardo Levi26, Celso Granato27, Giula M. Ferreira28, Ronaldo S. Francisco Jr8, Fabiana Granja29, Marcia T. Garcia30, Maria Luiza Moretti30, Mauricio W. Perroud Jr31, Terezinha M. P. P. Castineiras32, Carolina S. Lazari22, Sarah C. Hill1,33, Andreza A. de Souza Santos34, Camila L. Simeoni11, Julia Forato11, Andrei C. Sposito35, Angelica Z. Schreiber36, Magnun N. N. Santos36, Camila Zolini de Sá37, Renan P. Souza37, Luciana C. Resende-Moreira38, Mauro M. Teixeira39, Josy Hubner40, Patricia A. F. Leme41, Rennan G Moreira42, Maurício Lacerda Nogueira43, CADDE-Genomic-Network, Neil M Ferguson6, Silvia F. Costa2, José Luiz Proenca-Modena11, Ana Tereza R. Vasconcelos10, Samir Bhatt7, Philippe Lemey6, Chieh-Hsi Wu44, Andrew Rambaut45, Nick J. Loman20, Renato S. Aguiar37, Oliver G. Pybus1,, Ester C. Sabino2,?, and Nuno Rodrigues Faria1,2,7,?,*.*Correspondence to: sabinoec@usp.br and nuno.faria@zoo.ox.ac.uk ? These authors contributed equally to this work.This PDF file includes:Materials and MethodsFigs. S1 to S14Tables S1 to S3Captions for Data S1 and Data S2List of Members of the CADDE-Genomic-NetworkFull Reference List Materials and MethodsEthical statementResidual nasopharyngeal, tracheal and bronchial aspirate samples testing positive for SARS-CoV-2 by RT-qPCR were obtained from public health and private medical diagnostics laboratories (Table S1). All samples were de-identified before receipt by the researchers. Ethics approval for this study was approved by the national ethical review board (Comiss?o Nacional de ?tica em Pesquisa), protocol number CAAE 30127020.0.0000.0068.Epidemiological dataWe analysed case counts and deaths from the?Sistema de Informa??o da Vigil?ncia Epidemiológica da Gripe?-?Síndrome Respiratória Aguda Grave?(SIVEP-SARI). The SIVEP- SARI was created in 2009 for the H1N1 influenza pandemic and centralizes the notification of severe acute respiratory infection (SARI) cases for the Brazilian Ministry of Health.?The SIVEP- SARI database contains mostly hospitalized cases, while the non-hospitalized cases are mostly notified in the e-Sistema ?nico de Saúde (SUS) Vigil?ncia Epidemiológica database that is not available for public consultation. The SARI case definition that is used since 2012 includes hospitalized patients of any age, with a flu-like syndrome (fever and cough or throat pain) and that present dyspnoea or O2 saturation <95% or respiratory discomfort. Registered deaths due to SARI are also included independent of hospitalization. The SARI database has been made publicly available on a daily basis and can be retrieved at (accessed 1 June 2020).Estimates of human mobility flows Openly available Google Community Mobility Reports for Sao Paulo and Rio de Janeiro city were used to obtain an aggregated estimate of daily percent changes in mobility and includes changes in visits to places compared to baseline values for a 5-week period between Jan 3 – Feb 6, 2020 (available here: ) ADDIN EN.CITE <EndNote><Cite><Author>Aktay</Author><Year>2020</Year><RecNum>5442</RecNum><DisplayText>(<style face="italic">36</style>)</DisplayText><record><rec-number>5442</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591669017">5442</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Aktay, A., Bavadekar, S., Cossoul, G., Davis, J., Desfontaines, D., Fabrikant, A., Gabrilovich, E., Gadepalli, K., Gipson, B., Guevara, M., Kamath, C., Kansal, M., Lange, A., Mandayam, C., Oplinger, A., Pluntke, C., Roessler, T., Schlosberg, A., Shekel, T., Vispute, S., Vu, M., Wellenius, G., Williams, B., Wilson, R. J.</author></authors></contributors><titles><title>Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0)</title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>(36). We compare the Google Community Mobility Reports to temporally aggregated anonymised mobile phone mobility data that was freely provided by the Brazilian company InLoco. Data consists of pairs of origin-destination trips within and between states of Brazil,?with approximately 10 to 12 million trip records of more than 1km per day. Data was anonymized and pre-processed by the company, which has wide national coverage over Brazil, sampling approximately one fourth of the Brazilian population, focused on smartphone users, as previously described ADDIN EN.CITE <EndNote><Cite><Author>Oliveira</Author><Year>2020</Year><RecNum>5261</RecNum><DisplayText>(<style face="italic">37</style>)</DisplayText><record><rec-number>5261</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5261</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Oliveira, S. B., Porto, V. B. G., Ganem, F., Mendes, F. M., Almiron, M., Oliveira, W. K., Fontana, F., Almeida W. A. F., Junior, A. P. M. B., Pinheiro, H., N. B., Oliveira, . S., Andrrews, J. R., Faria, N. R., Lopes, M. B., Araujo, W. N., Quijano, F. A., Nakaya, H. I., Croda, J. </author></authors></contributors><titles><title>Monitoring social distancing and SARS-CoV-2 transmission in Brazil
using cell phone mobility data </title><secondary-title>medRxiv</secondary-title></titles><periodical><full-title>medRxiv</full-title></periodical><volume>doi: ;(37). Daily population-level mobility was measured for S?o Paulo and Rio de Janeiro from 1 January to 30 April 2020 (baseline January to February). InLoco mobility data can be found here: ADDIN EN.CITE <EndNote><Cite><Author>InLoco</Author><Year>2020</Year><RecNum>5440</RecNum><DisplayText>(<style face="italic">38</style>)</DisplayText><record><rec-number>5440</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591666878">5440</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>InLoco</author></authors></contributors><titles><title> (Website accessed in June 5, 2020)</title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>(38).Reproduction number using epidemiological and mobility dataA Bayesian semi-mechanistic model was used to estimate transmission intensity and attack rates of COVID-19 conditional on the reported number of deaths (available in ). Considering both cities Rio de Janeiro and S?o Paulo, four covariates related to mobility are considered. These describe the reduction or increase in mobility in parks (k=1), transit stations (k=2) and the average of groceries and pharmacies, retail and recreational areas, and workplaces (k=3). Their average was calculated because these variables are collinear.In addition, a social isolation index ADDIN EN.CITE <EndNote><Cite><Author>InLoco</Author><Year>2020</Year><RecNum>5440</RecNum><DisplayText>(<style face="italic">38</style>)</DisplayText><record><rec-number>5440</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591666878">5440</key></foreign-keys><ref-type name="Web Page">12</ref-type><contributors><authors><author>InLoco</author></authors></contributors><titles><title> (Website accessed in June 5, 2020)</title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>(38) recorded at city level was also introduced in the model as an additional covariate (k=4). This index was provided by InLoco, a Brazilian geolocation company, which gathers data from more than 60 million mobile devices spread in all areas of Brazil (see also previous section). The time-varying reproduction number Rt is modelled as a function of Google mobility variables and the city isolation index. The approach is similar to that described by Mellan et al. ADDIN EN.CITE <EndNote><Cite><Author>Mellan</Author><Year>2020</Year><RecNum>5005</RecNum><DisplayText>(<style face="italic">20</style>)</DisplayText><record><rec-number>5005</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452543">5005</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Mellan, T. A., Hoeltgebaum, H. H., Mishra, S., Whittaker, C., Schnekenberg, R. P., Gandy, A., Unwin, H. J. T., Vollmer, M. A. C., Coupland, H., Hawryluk, I., Faria, N. R., Vesga, J., Zhu, H., Hutchinson, M., Ratmann, O., Monod, M., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Brazeau, N., Charles, G., Cooper, L. V., Cucunuba, Z., Cuomo-Dannenburg, G., Dighe, A., Djaafara, B., Eaton, J., Elsland, S. L., FitzJohn, R., Fraser, K., Gaythorpe, K., Green, W., Hayes, S., Imai, N., Jeffrey, B., Knock, E., Laydon, D., Lees, J., Mangal, T., Mousa, A., Nedjati-Gilani, G., Nouvellet, P., Olivera, D., Parag, K. V., Pickles, M., Thompson, H. A., Verity, R., Walters, C., Wang, H., Wang, Y., Watson, O. J., Whittles, L., Xi, X., Okell, L., Dorigatti, I., Walker, P., Ghani, A., Riley, S., Ferguson, N. M., Donnelly, C. A., Flaxman, S., Bhatt, S.</author></authors></contributors><titles><title>Report 21 - Estimating COVID-19 cases and reproduction number in Brazil</title></titles><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>(20) but replaces the Google mobility residential covariate with the InLoco isolation index, on basis that a city level index is preferred over a state level one to model deaths at city level. In the end, we observed that the choice of using the InLoco isolation index over the Google residential index has marginal effect on the Rt predictions (not shown).Furthermore, to account for residual variation beyond that included in the mobility parameterization of Rt, a second order autoregressive (AR2) random process has been included in the model. The AR2 process accounts for residual correlation structure fitting random effects on a weekly basis, from the start of the epidemic in each city, up to the three weeks before the final time reported. For further details on implementation see ADDIN EN.CITE <EndNote><Cite><Author>Unwin</Author><Year>2020</Year><RecNum>5390</RecNum><DisplayText>(<style face="italic">18</style>)</DisplayText><record><rec-number>5390</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591558637">5390</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Unwin, H. J. T., Mishra, S., Bradley, V. C. Gandy, A., Vollmer, M. A., Mellan, T., Coupland, H., Ainslie, K., Whittaker, C., Ish-Horowicz, J., Filippi, S., Xi, X., Monod, M., Ratmann, O., Hutchinson, M., Valka, F., Zhu, H., Hawryluk, I., Milton, P., Baguelin, M., Boonyasiri, A., Brazeau, N., Cattarino, L., Charles, G., Cooper, L. V., Cucunuba, Z., Cuomo-Dannenburg, G., Djaafara, B., Dorigatti, I., Eales, O. J., Eaton, J., van Elsland, S., FitzJohn, R., Gaythorpe, K., Green, W., Hallett, T., Hinsley, W., Imai, N., Jeffrey, B., Knock, E., Laydon, D., Lees, J., Nedjati-Gilani, Nouvellet, P., Okell, L., Ower, A., Parag, K. V., Siveroni, I., Thompson, H. A., Verity, R., Walker, P., Walters, C., Wang, Y., Watson, O. J., Whittles, L., Ghani, A. Ferguson, N. M., Riley, S., Donnelly, C. A., Bhatt, S., Flaxman, S.</author></authors></contributors><titles><title>Report 23: State-level tracking of COVID-19 in the United States (21-05-2020)</title><secondary-title>doi: : ;(18). Model parameters were jointly estimated for both cities using partial pooling. Denote Ik,t,m as the k-th Google mobility indicator, at time t for city m. The time-varying reproduction number for city m, Rt,m, is modelled by:Rt,m=R0,m?2λ-1-k=14αk+βm,kIk,t,m+Bk-εm,wm(t)where λ-1 denotes the logistic function, αk the effects shared between M cities and βm,k city-specific effects. εm,wm(t) denotes a weekly (AR2) process that accounts for fitting extra variation not captured by the designated covariates. Bk denotes noise in the baseline and is set to Bk ~ Normal0,0.25. Prior distributions for the partial pooling model were set as:αk～Normal0,0.5βm,k～Normal0,γ, with γ～N0,0.5while the prior distribution for R0,m was chosen to be:R0,m～Normal3.28,κκ～Normal(0,0.5)with κ being the same between both cities to share information about the variability of R0,m. The value of 3.28 was used in PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GbGF4bWFuPC9BdXRob3I+PFllYXI+MjAyMDwvWWVhcj48
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ADDIN EN.CITE.DATA (41) (). PCR products were cleaned-up using AmpureXP purification beads (Beckman Coulter, United Kingdom) and quantified using fluorimetry with the Qubit dsDNA High Sensitivity assay on the Qubit 3.0 instrument (Life Technologies, USA). Amplicons from each sample were normalised and pooled in an equimolar fashion and barcoded using the EXP-NBD104 (1-12) and EXP-NBD114 (13-24) Native Barcoding Kits (Oxford Nanopore Technologies, UK). Whole genome sequencing and genome assemblySequencing libraries were generated using the SQK-LSK109 Kit (ONT, UK) and were loaded onto an R9.5.1 flow-cell (ONT, UK). RAMPART software from the ARTIC Network () was used to monitor the sequencing run in real-time to estimate the depth of coverage (200-fold) across the genome for each barcoded sample () and samples were sequenced between 8 to 48 hours. After the completion of the sequencing runs, fast5 files were basecalled, demultiplexed, and trimmed using Guppy software v2.2.7 (ONT, UK). The consensus genomes were obtained by the mapped of fastq files with the reference genome of SARS-CoV-2 isolate Wuhan-Hu 1 (GenBank Accession Number MN908947) using minimap2 v2.28.0) and converted to a sorted BAM file using SAMtools ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2009</Year><RecNum>5236</RecNum><DisplayText>(<style face="italic">42</style>)</DisplayText><record><rec-number>5236</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5236</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, H.</author><author>Handsaker, B.</author><author>Wysoker, A.</author><author>Fennell, T.</author><author>Ruan, J.</author><author>Homer, N.</author><author>Marth, G.</author><author>Abecasis, G.</author><author>Durbin, R.</author><author>Genome Project Data Processing, Subgroup</author></authors></contributors><auth-address>Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK, Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA.</auth-address><titles><title>The Sequence Alignment/Map format and SAMtools</title><secondary-title>Bioinformatics</secondary-title></titles><periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></periodical><pages>2078-9</pages><volume>25</volume><number>16</number><edition>2009/06/10</edition><keywords><keyword>Algorithms</keyword><keyword>Base Sequence</keyword><keyword>Computational Biology/*methods</keyword><keyword>Genome</keyword><keyword>Genomics</keyword><keyword>Molecular Sequence Data</keyword><keyword>Sequence Alignment/*methods</keyword><keyword>Sequence Analysis, DNA/*methods</keyword><keyword>*Software</keyword></keywords><dates><year>2009</year><pub-dates><date>Aug 15</date></pub-dates></dates><isbn>1367-4811 (Electronic)
1367-4803 (Linking)</isbn><accession-num>19505943</accession-num><urls><related-urls><url>;(42). Length filtering and the quality test was performed for each barcode using ARTIC guppyplex (). The genome statistics were obtained from samtools and the Tablet viewer ADDIN EN.CITE <EndNote><Cite><Author>Milne</Author><Year>2010</Year><RecNum>5237</RecNum><DisplayText>(<style face="italic">43</style>)</DisplayText><record><rec-number>5237</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5237</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Milne, I.</author><author>Bayer, M.</author><author>Cardle, L.</author><author>Shaw, P.</author><author>Stephen, G.</author><author>Wright, F.</author><author>Marshall, D.</author></authors></contributors><auth-address>Genetics Programme, Scottish Crop Research Institute, Invergowrie, Dundee, DD2 5DA, UK.</auth-address><titles><title>Tablet--next generation sequence assembly visualization</title><secondary-title>Bioinformatics</secondary-title></titles><periodical><full-title>Bioinformatics</full-title><abbr-1>Bioinformatics</abbr-1></periodical><pages>401-2</pages><volume>26</volume><number>3</number><edition>2009/12/08</edition><keywords><keyword>Base Sequence</keyword><keyword>Computational Biology/*methods</keyword><keyword>Databases, Genetic</keyword><keyword>Sequence Alignment</keyword><keyword>Sequence Analysis, DNA/*methods</keyword><keyword>*Software</keyword><keyword>User-Computer Interface</keyword></keywords><dates><year>2010</year><pub-dates><date>Feb 1</date></pub-dates></dates><isbn>1367-4811 (Electronic)
1367-4803 (Linking)</isbn><accession-num>19965881</accession-num><urls><related-urls><url>;(43) and to recover consensus sequences, called variants were detected with nanopolish. Individual nanopore sequencing statistics can be found in Data S1. Genome regions with a depth of <200-fold were not included in final consensus sequences, and these positions are represented with N characters.Quality control of genome consensus sequencesTo ensure the quality of our downstream analyses, we undertook stringent quality control steps on a total of 499 genomes generated for this study. First, only sequences with genome coverage above a given cut-off were included to guarantee highest possible phylogenetic accuracy of the resulting datasets. For Zika virus, which evolves at a similar pace compared to SARS-CoV-2, sequences with at least 30.0% genome coverage are required to provide sufficient phylogenetic and temporal resolution PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5UaGV6ZTwvQXV0aG9yPjxZZWFyPjIwMTg8L1llYXI+PFJl
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AG==
ADDIN EN.CITE.DATA (44). Given the current lack of SARS-CoV-2 genome coverage thresholds for phylogenetic studies, we used a conservative cut-off of 75.0% to guarantee phylogenetic accuracy. Thus, we removed 72 partial genomes (mean genome coverage of 46.9%, range 0.1 to 74.6%) from our data. We used MAFFT automatic algorithm to build multiple a sequence alignment of the resulting dataset ADDIN EN.CITE <EndNote><Cite><Author>Katoh</Author><Year>2014</Year><RecNum>5249</RecNum><DisplayText>(<style face="italic">45</style>)</DisplayText><record><rec-number>5249</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5249</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Katoh, K.</author><author>Standley, D. M.</author></authors></contributors><auth-address>Immunology Frontier Research Center, Osaka University, Suita, Japan.</auth-address><titles><title>MAFFT: iterative refinement and additional methods</title><secondary-title>Methods Mol Biol</secondary-title></titles><periodical><full-title>Methods Mol Biol</full-title><abbr-1>Methods in molecular biology</abbr-1></periodical><pages>131-46</pages><volume>1079</volume><edition>2013/10/31</edition><keywords><keyword>Computational Biology/*methods</keyword><keyword>Computers</keyword><keyword>DNA/genetics</keyword><keyword>RNA/genetics</keyword><keyword>Sequence Alignment/*methods</keyword><keyword>*Software</keyword></keywords><dates><year>2014</year></dates><isbn>1940-6029 (Electronic)
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ADDIN EN.CITE.DATA (44) with a gamma distribution to describe among-site variation in the rate of nucleotide substitution ADDIN EN.CITE <EndNote><Cite><Author>Yang</Author><Year>1994</Year><RecNum>5342</RecNum><DisplayText>(<style face="italic">46</style>)</DisplayText><record><rec-number>5342</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5342</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yang, Z.</author></authors></contributors><auth-address>Department of Zoology, Natural History Museum, London, United Kingdom.</auth-address><titles><title>Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods</title><secondary-title>J Mol Evol</secondary-title></titles><periodical><full-title>J Mol Evol</full-title><abbr-1>Journal of molecular evolution</abbr-1></periodical><pages>306-14</pages><volume>39</volume><number>3</number><edition>1994/09/01</edition><keywords><keyword>Animals</keyword><keyword>DNA, Mitochondrial/genetics</keyword><keyword>Genetic Variation/*genetics</keyword><keyword>Globins/genetics</keyword><keyword>Humans</keyword><keyword>Likelihood Functions</keyword><keyword>Mammals/genetics</keyword><keyword>*Models, Genetic</keyword><keyword>*Phylogeny</keyword><keyword>Point Mutation/genetics</keyword><keyword>Primates/genetics</keyword><keyword>RNA, Ribosomal/genetics</keyword></keywords><dates><year>1994</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0022-2844 (Print)
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ADDIN EN.CITE.DATA (47). We then regressed root-to-tip genetic divergence against sampling dates to investigate temporal signal of our datasets and to identify sequences with low data quality (e.g. with assembling issues, sequencing and alignment errors, data annotation errors and sample contamination) ADDIN EN.CITE <EndNote><Cite><Author>Rambaut</Author><Year>2016</Year><RecNum>5241</RecNum><DisplayText>(<style face="italic">48</style>)</DisplayText><record><rec-number>5241</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5241</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rambaut, A.</author><author>Lam, T. T.</author><author>Max Carvalho, L.</author><author>Pybus, O. G.</author></authors></contributors><auth-address>Institute of Evolutionary Biology,; Centre for Immunity, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, King's Buildings, Edinburgh EH9 3JT, UK.
School of Public Health, University of Hong Kong, Hong Kong SAR, China and.
Institute of Evolutionary Biology.
Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK.</auth-address><titles><title>Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen)</title><secondary-title>Virus Evol</secondary-title></titles><periodical><full-title>Virus Evol</full-title></periodical><pages>vew007</pages><volume>2</volume><number>1</number><edition>2016/10/25</edition><keywords><keyword>evolutionary rate</keyword><keyword>model selection</keyword><keyword>molecular clock</keyword><keyword>phylogeny</keyword><keyword>regression</keyword></keywords><dates><year>2016</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>2057-1577 (Print)
2057-1577 (Linking)</isbn><accession-num>27774300</accession-num><urls><related-urls><url>;(48). No obvious outliers were identified in this step. Finally, we assessed genome sequence quality through quality control scores and identified virus lineages using Pangolin (hCoV-2019/pangolin) and CoV-GLUE (cov-glue.cvr.gla.ac.uk/). All sequences passed?the quality control steps. Collation of SARS-CoV-2 global datasets Our genome dataset contains 427 near-complete and complete new genomes from 18 out of 27 Brazilian states. We appended this data to 63 other Brazilian genomes available in GISAID until May 5, 2020 (), generating a dataset of 490 Brazilian genomes that covers 21 out of the 27 Brazilian states. Sampling collection dates of Brazilian sequences ranged from February 25 2020 [first reported case in Brazil ADDIN EN.CITE <EndNote><Cite><Author>Jesus</Author><Year>2020</Year><RecNum>4984</RecNum><DisplayText>(<style face="italic">49</style>)</DisplayText><record><rec-number>4984</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452543">4984</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jesus, J. G., Sacchi, C., Candido, D. D. S., 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., Goncalves, 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.</author></authors></contributors><titles><title>Importation and early local transmission of COVID-19 in Brazil, 2020</title><secondary-title>Rev Inst Med Trop Sao Paulo</secondary-title></titles><periodical><full-title>Rev Inst Med Trop Sao Paulo</full-title><abbr-1>Revista do Instituto de Medicina Tropical de Sao Paulo</abbr-1></periodical><volume>62</volume><number>e30</number><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>(49)] to 30 April 2020. The Brazilian datasets represent approximately 1 sequence for every 200 cases (0.5%) notified up to April 30, 2020; including 3.6% of all cases notified in the city of Sao Paulo as of 5 April 2020 (the day of our most recent sequence from Sao Paulo), 1.27% of all cases notified in the city of Rio de Janeiro as of 24 April 2020 and 3.13% of all cases notified in the city Fortaleza as of 6 April 2020 (the day of our most recent sequence from Fortaleza). Representativity of the genome data with regards to the number of cumulative SARS-CoV-2 cases in each state up until the date of the last sequenced sample (30 April 2020) can be found in Fig. 2A and Fig. S2. We appended Brazilian data to two global datasets prepared from genome data deposited in GISAID (). The first global dataset contains 710 subsampled sequences to include one genome per country per day (based on sample collection day) available until April 24, 2020 (named hereafter as “subsampled dataset”). The second dataset (“full dataset”) contains 13,393 sequences made available until May 5, 2020. In silico analysis of molecular diagnostic assays The presence of frequently identified mismatches in several diagnostic RT-qPCR assays suggests that certain assays may be less appropriate for use in Brazil than other diagnostic assays in which no mismatches were identified. We used a custom Python script to analyse mismatches between sequences of Brazilian SARS-CoV-2 genomes to sequences of primers and probes used in 13 common assays PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3JtYW48L0F1dGhvcj48WWVhcj4yMDIwPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA (21, 50-55).The relevant binding site sequence in every genome was compared to each primer or probe sequence, and the position and bases of any mismatching sites were recorded. If any unknown bases (i. e., Ns) were present in the binding site sequence, that genomic sequence was excluded in analyses of the relevant primer or probe. If other (i.e., non-N) ambiguous bases were present in the primer or probe or within the genomic binding site, the genome was not excluded. In such cases, a mismatch is recorded if the set of bases allowable by the primer/probe sequence does not intersect with the set of bases allowable by the genomic binding site sequence. For each primer, we plotted the proportion of mismatching bases at each site in the genomes considered from the alignment, and coloured by the mismatching base in the Brazilian sequence (Fig. S5).Phylogenetic analysis of SARS-CoV-2 in BrazilMaximum likelihood phylogenies were estimated for the global subsampled and full datasets in IQTree v.2 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NaW5oPC9BdXRob3I+PFllYXI+MjAyMDwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (44) as described above. We obtained branch supports with the ultrafast bootstrap approach ADDIN EN.CITE <EndNote><Cite><Author>Hoang</Author><Year>2018</Year><RecNum>5263</RecNum><DisplayText>(<style face="italic">56</style>)</DisplayText><record><rec-number>5263</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5263</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hoang, D. T.</author><author>Chernomor, O.</author><author>von Haeseler, A.</author><author>Minh, B. Q.</author><author>Vinh, L. S.</author></authors></contributors><auth-address>Faculty of Information Technology, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam.
Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University Vienna, Vienna, Austria.
Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria.</auth-address><titles><title>UFBoot2: Improving the Ultrafast Bootstrap Approximation</title><secondary-title>Mol Biol Evol</secondary-title></titles><periodical><full-title>Mol Biol Evol</full-title><abbr-1>Molecular biology and evolution</abbr-1></periodical><pages>518-522</pages><volume>35</volume><number>2</number><edition>2017/10/28</edition><keywords><keyword>*Likelihood Functions</keyword><keyword>Models, Genetic</keyword><keyword>*Phylogeny</keyword><keyword>*Software</keyword><keyword>*maximum likelihood</keyword><keyword>*model violation</keyword><keyword>*phylogenetic inference</keyword><keyword>*polytomies</keyword><keyword>*ultrafast bootstrap</keyword></keywords><dates><year>2018</year><pub-dates><date>Feb 1</date></pub-dates></dates><isbn>1537-1719 (Electronic)
0737-4038 (Linking)</isbn><accession-num>29077904</accession-num><urls><related-urls><url>;(56). As recombination is a relatively frequent evolutionary mechanism in coronaviruses, we screened our datasets for recombination using the Phi-test approach ADDIN EN.CITE <EndNote><Cite><Author>Bruen</Author><Year>2006</Year><RecNum>480</RecNum><DisplayText>(<style face="italic">57</style>)</DisplayText><record><rec-number>480</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452534">480</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bruen, T. C.</author><author>Philippe, H.</author><author>Bryant, D.</author></authors></contributors><auth-address>McGill Centre for Bioinformatics, McGill University, Montreal, Quebec, Canada. trevor@mcb.mcgill.ca</auth-address><titles><title>A simple and robust statistical test for detecting the presence of recombination</title><secondary-title>Genetics</secondary-title><alt-title>Genetics</alt-title></titles><periodical><full-title>Genetics</full-title><abbr-1>Genetics</abbr-1></periodical><alt-periodical><full-title>Genetics</full-title><abbr-1>Genetics</abbr-1></alt-periodical><pages>2665-81</pages><volume>172</volume><number>4</number><keywords><keyword>Computer Simulation</keyword><keyword>Genetics, Population</keyword><keyword>Genotype</keyword><keyword>Likelihood Functions</keyword><keyword>Linkage Disequilibrium</keyword><keyword>Models, Genetic</keyword><keyword>Models, Statistical</keyword><keyword>Mutation</keyword><keyword>Probability</keyword><keyword>*Recombination, Genetic</keyword></keywords><dates><year>2006</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>0016-6731 (Print)
0016-6731 (Linking)</isbn><accession-num>16489234</accession-num><urls><related-urls><url>;(57) in SplitsTree ADDIN EN.CITE <EndNote><Cite><Author>Huson</Author><Year>2006</Year><RecNum>1534</RecNum><DisplayText>(<style face="italic">58</style>)</DisplayText><record><rec-number>1534</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452536">1534</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Huson, D. H.</author><author>Bryant, D.</author></authors></contributors><auth-address>Center for Bioinformatics (ZBIT), Tubingen University, Tubingen, Germany. huson@informatik.uni-tuebingen.de</auth-address><titles><title>Application of phylogenetic networks in evolutionary studies</title><secondary-title>Mol Biol Evol</secondary-title><alt-title>Molecular biology and evolution</alt-title></titles><periodical><full-title>Mol Biol Evol</full-title><abbr-1>Molecular biology and evolution</abbr-1></periodical><alt-periodical><full-title>Mol Biol Evol</full-title><abbr-1>Molecular biology and evolution</abbr-1></alt-periodical><pages>254-67</pages><volume>23</volume><number>2</number><edition>2005/10/14</edition><keywords><keyword>Animals</keyword><keyword>*Evolution, Molecular</keyword><keyword>*Models, Genetic</keyword><keyword>*Phylogeny</keyword></keywords><dates><year>2006</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0737-4038 (Print)
0737-4038 (Linking)</isbn><accession-num>16221896</accession-num><urls><related-urls><url>;(58) and all available methods in RDP v. 4 ADDIN EN.CITE <EndNote><Cite><Author>Martin</Author><Year>2015</Year><RecNum>3235</RecNum><DisplayText>(<style face="italic">59</style>)</DisplayText><record><rec-number>3235</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452540">3235</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Martin, D. P.</author><author>Murrell, B.</author><author>Golden, M.</author><author>Khoosal, A.</author><author>Muhire, B.</author></authors></contributors><auth-address>Department of Integrative Biomedical Sciences, Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Anzio Road Observatory 7549, Cape Town, South Africa.
Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA.
Department of Statistics, University of Oxford, 1 South Parks Road, OX1 3TG, Oxford, UK.</auth-address><titles><title>RDP4: Detection and analysis of recombination patterns in virus genomes</title><secondary-title>Virus Evol</secondary-title></titles><periodical><full-title>Virus Evol</full-title></periodical><pages>vev003</pages><volume>1</volume><number>1</number><keywords><keyword>horizontal gene transfer</keyword><keyword>lateral gene transfer</keyword><keyword>reassortment</keyword><keyword>sequence analysis software</keyword></keywords><dates><year>2015</year></dates><isbn>2057-1577 (Linking)</isbn><accession-num>27774277</accession-num><urls><related-urls><url>;(59). No evidence of recombination was found in either datasets. Dated phylogenies were estimated under HKY + Γ nucleotide substitution model and a strict molecular clock in BEAST v.1.10.4 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdWNoYXJkPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48
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1063-5157 (Linking)</isbn><accession-num>21963610</accession-num><urls><related-urls><url>;(61) in duplicate for a length of 250 million Markov chain Monte Carlo (MCMC) steps using both a parametric exponential growth tree prior and a non-parametric skygrid tree prior ADDIN EN.CITE <EndNote><Cite><Author>Gill</Author><Year>2013</Year><RecNum>5341</RecNum><DisplayText>(<style face="italic">62</style>)</DisplayText><record><rec-number>5341</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5341</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gill, M. S.</author><author>Lemey, P.</author><author>Faria, N. R.</author><author>Rambaut, A.</author><author>Shapiro, B.</author><author>Suchard, M. A.</author></authors></contributors><auth-address>Department of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, Los Angeles, USA.</auth-address><titles><title>Improving Bayesian population dynamics inference: a coalescent-based model for multiple loci</title><secondary-title>Mol Biol Evol</secondary-title></titles><periodical><full-title>Mol Biol Evol</full-title><abbr-1>Molecular biology and evolution</abbr-1></periodical><pages>713-24</pages><volume>30</volume><number>3</number><edition>2012/11/28</edition><keywords><keyword>Algorithms</keyword><keyword>Bayes Theorem</keyword><keyword>Computer Simulation</keyword><keyword>Evolution, Molecular</keyword><keyword>Genes, Viral</keyword><keyword>*Genetic Loci</keyword><keyword>Genetic Speciation</keyword><keyword>HIV-1/genetics</keyword><keyword>Humans</keyword><keyword>Markov Chains</keyword><keyword>*Models, Genetic</keyword><keyword>Monte Carlo Method</keyword><keyword>Mutation</keyword><keyword>Population Density</keyword><keyword>Population Dynamics</keyword><keyword>Statistics, Nonparametric</keyword></keywords><dates><year>2013</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>1537-1719 (Electronic)
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ADDIN EN.CITE.DATA (60) without enforcing monophyly.Temporal phylogeographyTo reconstruct geographic history of SARS-CoV-2 in Brazil we modelled instantaneous transitions between locations in the global subsampled dataset using a discrete asymmetric phylogeographic approach ADDIN EN.CITE <EndNote><Cite><Author>Lemey</Author><Year>2009</Year><RecNum>5343</RecNum><DisplayText>(<style face="italic">65</style>)</DisplayText><record><rec-number>5343</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5343</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lemey, P.</author><author>Rambaut, A.</author><author>Drummond, A. J.</author><author>Suchard, M. A.</author></authors></contributors><auth-address>Department of Microbiology and Immunology, Katholieke Universiteit Leuven, Leuven, Belgium. philippe.lemey@uz.kuleuven.be</auth-address><titles><title>Bayesian phylogeography finds its roots</title><secondary-title>PLoS Comput Biol</secondary-title></titles><periodical><full-title>PLoS Comput Biol</full-title><abbr-1>PLoS computational biology</abbr-1></periodical><pages>e1000520</pages><volume>5</volume><number>9</number><edition>2009/09/26</edition><keywords><keyword>Animals</keyword><keyword>*Bayes Theorem</keyword><keyword>Computational Biology/methods</keyword><keyword>Dogs</keyword><keyword>*Geography</keyword><keyword>Humans</keyword><keyword>Influenza A Virus, H5N1 Subtype/genetics</keyword><keyword>Influenza, Human/epidemiology</keyword><keyword>Markov Chains</keyword><keyword>*Models, Biological</keyword><keyword>Molecular Epidemiology/*methods</keyword><keyword>*Phylogeny</keyword><keyword>Rabies/epidemiology</keyword><keyword>Rabies virus/genetics</keyword><keyword>Stochastic Processes</keyword></keywords><dates><year>2009</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1553-7358 (Electronic)
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ADDIN EN.CITE.DATA (66). We considered several discretization schemes. First, taxa were assigned to two locations, “Brazil” and “Others” (k=2, scheme A). Second, taxa were assigned to the following locations: “North America”, “Europe”, “Asia”, “Oceania”, “Africa”, and to the five Brazilian regions of “Southeast”, “Northeast”, “North”, “Centre-West”, “South” (k=10, scheme B). Thirdly, we considered movement across states in Brazil (k=21, scheme C). We then estimated the number of migration events over time on a branch-by-branch basis using a Markov jumps PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5PJmFwb3M7QnJpZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z
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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5PJmFwb3M7QnJpZW48L0F1dGhvcj48WWVhcj4yMDA5PC9Z
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ADDIN EN.CITE.DATA (60). Discretization scheme A was used to quantify number of virus lineage introductions in Brazil (see annotated tree in Fig. S8). To count the number of migrations (i) from locations outside Brazil to any Brazilian location and (ii) from one Brazilian location to another Brazilian location, relative to the total number of transitions during the same time interval we used scheme B (Fig. 3B). Finally, to investigate source-sink dynamics of virus spread within Brazil, we estimate the number of transitions into and out of each state considering locations discretized as in scheme C (Fig. S7). To estimate changes in the velocity of virus lineages in Brazil, we used a flexible relaxed random walk (RRW) diffusion model that accommodates branch-specific variation in rates of dispersal with a Cauchy distribution ADDIN EN.CITE <EndNote><Cite><Author>Lemey</Author><Year>2010</Year><RecNum>5262</RecNum><DisplayText>(<style face="italic">70</style>)</DisplayText><record><rec-number>5262</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5262</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lemey, P.</author><author>Rambaut, A.</author><author>Welch, J. J.</author><author>Suchard, M. A.</author></authors></contributors><auth-address>Department of Microbiology and Immunology, Katholieke Universiteit Leuven, Leuven, Belgium. philippe.lemey@uz.kuleuven.ac.be</auth-address><titles><title>Phylogeography takes a relaxed random walk in continuous space and time</title><secondary-title>Mol Biol Evol</secondary-title></titles><periodical><full-title>Mol Biol Evol</full-title><abbr-1>Molecular biology and evolution</abbr-1></periodical><pages>1877-85</pages><volume>27</volume><number>8</number><edition>2010/03/06</edition><keywords><keyword>Animals</keyword><keyword>*Bayes Theorem</keyword><keyword>*Biological Evolution</keyword><keyword>Disease Outbreaks</keyword><keyword>*Geography</keyword><keyword>*Models, Genetic</keyword><keyword>*Phylogeny</keyword><keyword>Rabies/epidemiology</keyword><keyword>Raccoons</keyword><keyword>*Stochastic Processes</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1537-1719 (Electronic)
0737-4038 (Linking)</isbn><accession-num>20203288</accession-num><urls><related-urls><url>;(70). We focus on Brazilian clades with three or more strains and fix a Bayesian MCC phylogeographic tree, similar to recently described ADDIN EN.CITE <EndNote><Cite><Author>Dellicour</Author><Year>2020</Year><RecNum>5336</RecNum><DisplayText>(<style face="italic">35</style>)</DisplayText><record><rec-number>5336</rec-number><foreign-keys><key app="EN" db-id="sff9ts52a505rhet59b5eettwssr2rzw55se" timestamp="1591452544">5336</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dellicour, S., Durkin, K., Hong, S. L., Vanmechelen, B., Marti-Carreras, J., Gill, M. S., Meex, C., Bontems, S., Andre, E., Gilbert, M., Walker, C., de Maio, N., Hadfield, J., Hayette, P., Bours, V., Wawina-Bokalanga, T., Artesi, M., Baele, G., Maes, P.</author></authors></contributors><titles><title>A phylodynamic workflow to rapidly gain insights into the dispersal history and dynamics of SARS-CoV-2 lineages</title><secondary-title>bioRxiv</secondary-title></titles><periodical><full-title>bioRxiv</full-title></periodical><volume>;(35). In brief, for each sequence, latitude and longitude randomly attributed from the patient’s municipality of residence. MCMC chains were run for 10 million steps and sampled every 1000th step, with convergence assessed using Tracer v1.7 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYW1iYXV0PC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48
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ADDIN EN.CITE.DATA (71, 72) to extract and map spatiotemporal information embedded in posterior trees. Air travel mobility dataData on air passenger flows were analysed using public data produced by Brazil’s Civil Aviation Agency (ANAC). These data bring detailed information, including number of passengers and connections, for every international flight to and from Brazil as well as national flights within the country. The data set is available at ?. Using ANAC’s microdata, we calculated the daily number of passengers who disembarked or had a national connection in each Brazilian city between January and April in 2019 and 2020 disaggregated by airport and country of origin. When aggregating international passenger flows, we considered the final destination of passengers where they disembarked whenever this information was available. Information was missing for 23.254?(60%) international flights that arrived in Brazil between January and March of?2019 and 2020. As a result, the numbers of?passengers arriving in?international airport hubs in Brazil?might be?overestimated. A related limitation of this database is that it does not track individual trajectories. Consequently, passengers who make connections on flights with different numbers are counted in the origin-destination pair of the last leg of the trip.AcknowledgmentsWe thank GISAID database for supporting rapid and transparent sharing of genomic data during the COVID-19 pandemic. A full list acknowledging those involved in the generation of sequences used in this study can be found in Data S2. We thank Lucy Matkin for logistic support to the CADDE project. FundingThis work was made possible by the support of a Medical Research Council and Funda??o de Amparo à Pesquisa do Estado de S?o Paulo CADDE partnership award (MR/S0195/1) and a John Fell Research Fund (grant 005166). N.R.F. is supported by a Wellcome Trust and Royal Society Sir Henry Dale Fellowship (204311/Z/16/Z). D.D.S.C. is supported by the Clarendon Fund and by the Department of Zoology of the University Oxford. Fig. S1. Sequencing statistics. Distribution of the number of mapped reads, average depth coverage, number of bases whose coverage is above 25x and percentage of covered bases for sequences generated by this study. Fig. S2. Distribution of Brazilian SARS-CoV-2 genomes (n=427) by state and collection date. (A) SARS-CoV-2 genomes are grouped according to federal state of sample collection. Sampling per state was proportional to the number of severe acute respiratory illness (SARI) cases for each state. (B) Date of SARS-CoV-2 genomes grouped according to federal state of collection. Colours represent federal state of origin. Dates range from the 25 February (first Brazilian case) to the 30 of April 2020. Fig. S3. Genome coverage plotted against RT-qPCR cycle threshold. Each circle corresponds to a sequenced genome with 20-fold coverage above 75%. Each sample is coloured by the number of days between onset of symptoms (red to blue) to date of sample collection. Circles with no colour indicate samples for which information on onset of symptoms was unavailable.Fig. S4. Spatial representativity of the genome data generated in this study (n=427) and publicly available sequences from Brazil available in GISAID (n=63). SARI cases and deaths are SARS-CoV-2 confirmed cases plus cases with unknown specified aetiology excluding those cases that tested positive for other respiratory pathogens. SARS-CoV-2 SARI cases and deaths correspond to cases and deaths confirmed to be SARS-CoV-2 positive using molecular, clinical and epidemiological criteria. Epidemiological data is available at . Cumulative cases until the 30 April 2020 (date of the most recent genome sequence) were used for the correlation analysis.Fig. S5. In silico analysis of primer/probe mismatches to Brazilian strains. Proportion of Brazilian SARS-CoV-2 genomes used on this study that are not exactly complementary to primer/probe bases for each primer/probe site. The number of genomes considered here is variable for each primer or probe because genomes with Ns in primer or probe binding sites were excluded from analyses (as detailed in the?Supplementary Materials and Methods). This number, and the sequence information for each primer or probe, is given in?Table S2. Site positions are given relative to the primer/probe sequence (5’ to 3’). Colours represent bases in the Brazilian SARS-CoV-2 genomes that do not match the primer/probe sequence. Note that the number of positions displayed on the x-axis for each assay is given as the length of the longest primer or probe in that assay, and therefore not all marked site positions with lack of mismatches displayed are relevant.?Fig. S6. Distribution of SARS-CoV-2 lineages per Brazilian state. SARS-CoV-2 lineage identification for 490 Brazilian genomes was performed using Pangolin (hCoV-2019/pangolin) and CoV-GLUE (cov-glue.cvr.gla.ac.uk/) tools. States are defined by their 2-letter ISO 3166-1 codes.Fig. S7. Maximum likelihood phylogenies estimated using the global subsampled dataset (n=1,182) were coloured according to two schemes: (A) “Brazil” (n =490) and “Others” (n=692) (k=2, scheme A) and (B) “CADDE study” (n=427) and “Other studies” (n =755) (k=2). Fig. S8. Detailed annotated maximum clade phylogenetic tree. Time-resolved maximum clade credibility phylogeny of 1,182 SARS-CoV-2 sequences, 490 from Brazil (salmon) and 692 from outside Brazil (blue). The largest Brazilian clusters are highlighted in grey (Clade 1, Clade 2 and Clade 3). States are defined by their 2-letter ISO 3166-1 codes. MCC tree can be found (see also Data Availability). Fig. S9. Estimated proportions of geographic transition events for each Brazilian state. Transitions have been estimated using a phylogeographic approach with Markov Jumps implemented in BEAST v1.10.14 (see methods for details). For each state the proportion of international (imports and exports) and national (imports and exports) were calculated from the total estimated for each event type. Blue denotes international events, while pink denotes national events. Figure S10. Detailed view of main Brazilian phylogenetic clusters. Maximum clade credibility tree was generated from 490 Brazilian genomes plus 692 genomes from other countries under a phylogeographic discrete trait analysis implemented in BEAST v1.10.14 (for details, see methods section). Colours are assigned according to Brazilian state. Grey colour was assigned to branches with high location uncertainty. States are defined by their 2-letter ISO 3166-1 codes (see also caption of Fig. 1).Fig. S11. Dates of emergence of SARS-CoV-2 main clades in Brazil were estimated using a parametric exponential growth (left) and a non-parametric Skygrid model (right). Dated phylogenies were estimated under HKY + Γ nucleotide substitution model and a strict molecular clock in BEAST v.1.10.4 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdWNoYXJkPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48
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ADDIN EN.CITE.DATA (60) that assumes constant evolutionary rates throughout the phylogeny.Figure S12. Geographic distribution of Brazilian SARS-CoV-2 clusters. 490 SARS-CoV-2 Brazilian sequences were grouped according to Brazilian state of collection and SARS-CoV-2 phylogenetic cluster as identified from the Maximum Clade Credibility (MCC) tree generated under a Bayesian Phylogenetic approach using BEAST v1.10.14 (for details, see methods section). States are defined by their 2-letter ISO 3166-1 codes.Fig. S13. Impact of the COVID-19 epidemic in international travel to Brazil. Number of inbound international passengers flying to Brazil for the top countries of origin as made available by the National Civil Aviation Agency of Brazil (ANAC). Dark grey dots represent the daily number of passengers in 2019. Colourful plots and lines represent the number of passengers and the trend line for 2020 respectively. Dotted grey line shows the day of the first confirmed case in Brazil.Figure S14. SARS-CoV-2 spread in southeast Brazil before (left) and after (right) Tshift. Zoomed version of the maps on the main Figure 3. Circles represent nodes of the MCC phylogeny and are coloured according to their inferred time of occurrence. Shaded areas represent the 80% high posterior density (HPD) interval and depict the uncertainty of the phylogeographic estimates for each node. Solid curved lines denote the links between sequences and the directionality of movement. In addition to the clusters included in the continuous analysis, sequences belonging to clusters with less than 3 sequences were also plotted in the map with no lines connecting them (for details, see methods). Table S1. City-level estimates of time-varying reproduction number Rt for S?o Paulo and Rio de Janeiro based on deaths reported in the SARI SARS-CoV-2 dataset (available at , accessed 1 June 2020). Rt is estimated on 4 May 2020 with 95% Bayesian Credible Intervals (BCIs). 7-day mean values are also given from 27 April 2020 to 4 May 2020.CityRt 95% BCIRt 95% BCI 7-day averageS?o Paulo1.3 (1.0, 1.6)1.2 (0.9, 1.7)Rio de Janeiro1.3 (1.0, 1.6)1.2 (0.9, 1.5)Table S2. Genomic laboratories and protocols involved in this study. Ct = real-time quantitative polymerase chain reaction (RT-qPCR) cycle threshold. No. = number. Individual sample level information on sequenced data can be found in Data S1. See also Table S2 for detailed information on the assays used here for molecular diagnostic. SequencingInstitutionSample TypeDiagnostic ProtocolSequencing ProtocolNo. generated genomesIMT-USPNPS/BAL (n=32), NPS (n=112), NPS/OPS (n=21), OPS (n=3),TS (n=6)CharitéARTIC v1 (n=7), v2 (n=210) and v3 (n=56)273UFRJ-LNCCNPS (n=36),NPS/OPS (n=52)Charitéand CDC USAARTIC v3 (n=88)88UNICAMPBAL (n=1), NPS (n=64), TA (n=1)CharitéARTIC v3 (n=66)66Table S3. Assay, and sequence information for each primer of probe included in the in silico analysis. N represents the number of Brazilian sequences (total of 490) with sufficient information at given primer of probe binding sites.AssayPrimer/probeSequenceN2019-nCoV_N12019-nCoV_N1-FGACCCCAAAATCAGCGAAAT4792019-nCoV_N12019-nCoV_N1-PACCCCGCATTACGTTTGGTGGACC4782019-nCoV_N12019-nCoV_N1-RTCTGGTTACTGCCAGTTGAATCTG4782019-nCoV_N22019-nCoV_N2-FTTACAAACATTGGCCGCAAA4352019-nCoV_N22019-nCoV_N2-PACAATTTGCCCCCAGCGCTTCAG4342019-nCoV_N22019-nCoV_N2-RGCGCGACATTCCGAAGAA4342019-nCoV_N32019-nCoV_N3-FGGGAGCCTTGAATACACCAAAA4732019-nCoV_N32019-nCoV_N3-PAYCACATTGGCACCCGCAATCCTG4782019-nCoV_N32019-nCoV_N3-RTGTAGCACGATTGCAGCATTG478E_SarbecoE_Sarbeco_F1ACAGGTACGTTAATAGTTAATAGCGT489E_SarbecoE_Sarbeco_P1ACACTAGCCATCCTTACTGCGCTTCG479E_SarbecoE_Sarbeco_R2ATATTGCAGCAGTACGCACACA479HKU_NHKU-NFTAATCAGACAAGGAACTGATTA434HKU_NHKU-NPGCAAATTGTGCAATTTGCGG435HKU_NHKU-NRCGAAGGTGTGACTTCCATG434HKU_ORF1b-nsp14HKU-ORF1b-nsp14FTGGGGYTTTACRGGTAACCT481HKU_ORF1b-nsp14HKU-ORF1b-nsp14PTAGTTGTGATGCWATCATGACTAG484HKU_ORF1b-nsp14HKU-ORF1b-nsp14RAACRCGCTTAACAAAGCACTC485NN_FGGGGAACTTCTCCTGCTAGAAT463NN_PTTGCTGCTGCTTGACAGATT470NN_RCAGACATTTTGCTCTCAAGCTG460N_SarbecoN_Sarbeco_F1CACATTGGCACCCGCAATC478N_SarbecoN_Sarbeco_P1ACTTCCTCAAGGAACAACATTGCCA466N_SarbecoN_Sarbeco_R1GAGGAACGAGAAGAGGCTTG471NIID_2019-nCOV_NNIID_2019-nCOV_N_F2AAATTTTGGGGACCAGGAAC436NIID_2019-nCOV_NNIID_2019-nCOV_N_P2ATGTCGCGCATTGGCATGGA435NIID_2019-nCOV_NNIID_2019-nCOV_N_R2TGGCAGCTGTGTAGGTCAAC438ORF1abORF1ab_FCCCTGTGGGTTTTACACTTAA488ORF1abORF1ab_PCCGTCTGCGGTATGTGGAAAGGTTATGG298ORF1abORF1ab_RACGATTGTGCATCAGCTGA295RdRP_SARSrRdRP_SARSr-FGTGARATGGTCATGTGTGGCGG490RdRP_SARSrRdRP_SARSr-P2CAGGTGGAACCTCATCAGGAGATGC490RdRP_SARSrRdRP_SARSr-RCARATGTTAAAWACACTATTAGCATA490WH-NICNWH-NICN-FCGTTTGGTGGACCCTCAGAT478WH-NICNWH-NICN-PCAACTGGCAGTAACCA478WH-NICNWH-NICN-RCCCCACTGCGTTCTCCATT477Wuhan-TM2020Wuhan-TM2020ForTCGTGCTACAACTTCCTCAAG467Wuhan-TM2020Wuhan-TM2020ProbeCCGCCTCTGCTCCCTTCTGC470Wuhan-TM2020Wuhan-TM2020RevCTGCCWGGAGTTGAATTTCTTG471Data S1 (separate file)Detailed information on all 1,182 sequences used in this study. File contains information on epidemiology, demography, location, diagnostics, sequencing statistics and evolution of 427 SARS-CoV-2 sequences generated in this study and 755 sequences downloaded from GISAID.Data S2 (separate file)Acknowledgment GISAID table. File contains Accession ID, collection date, originating and submitting lab and authors, from . Members of the CADDE-Genomic-NetworkCynthia Chester Cardoso, Orlando da Costa Ferreira Jr., Rodrigo Moraes Brindeiro, Diana Mariani, Alice Laschuk Herlinger, André Felipe Andrade dos Santos, Anna Carla Pinto Castineiras, Camila de Almeida Velozo, Camila Nacif, Camille Victoria Leal Correia da Silva, Caroline Macedo Nascimento, Cassia Cristina Alves Gon?alves, Cíntia Policarpo, Débora Souza Faffe, Ekaterine Sim?es Goudoris, Elaine Sobral, Elisangela Costa da Silva, ?rica Ramos dos Santos Nascimento, Fabio Hecht Castro Medeiros, Fábio Luís Lima Monteiro, Fernando Luz de Castro, Francine Bittencourt Schiffler, Guilherme Sant'Anna de Lira, Helena Keito Toma, Huang Ling Fang, Ingrid Camelo da Silva, Isabela de Carvalho Labarba, Isabela de Carvalho Leit?o, Jessica Maciel de Almeida, Joissy Aprigio de Oliveira, Juliana Cazarin de Menezes, Juliana Tiemi Sato Fortuna, Karyne Ferreira Monteiro, Lendel Correia da Costa, Lídia Theodoro Boullosa, Liliane Tavares de Faria Cavalcante, Lucas Matos Millioni, Luciana Jesus da Costa, Marcelo Calado de Paula T?rres, Matheus Augusto Calvano Cosentino, Mayla Gabryele Miranda de Melo, Mirela D'arc Ferreira da Costa, Pedro Henrique Costa da Paz, Pedro Telles Calil, Rafael de Mello Galliez, Richard Araujo Maia, Sergio Lisboa Machado, Thamiris dos Santos Miranda, Victor Akira Ota, Viviane Guimar?es Gomes, Gislaine Celestino Dutra Silva, Marilia Mazzi Moraes Full List of References ADDIN EN.REFLIST 1.K. 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