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

Darlan S. Candido, Ingra M. Claro, Jaqueline G. de Jesus and 76 more

Science · 2020 · CC BY

doi:10.1101/2020.06.11.20128249

Abstract

The spread of SARS-CoV-2 in Brazil Brazil has been hard-hit by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Candido et al. combined genomic and epidemiological analyses to investigate the impact of nonpharmaceutical interventions (NPIs) in the country. By setting up a network of genomic laboratories using harmonized protocols, the researchers found a 29% positive rate for SARS-CoV-2 among collected samples. More than 100 international introductions of SARS-CoV-2 into Brazil were identified, including three clades introduced from Europe that were already well established before the implementation of NPIs and travel bans. The virus spread from urban centers to the rest of the country, along with a 25% increase in the average distance traveled by air passengers before travel bans, despite an overall drop in short-haul travel. Unfortunately, the evidence confirms that current interventions remain insufficient to keep virus transmission under control in Brazil. Science , this issue p. 1255

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 17, 2026. Paragraph numbers are ours, so an answer can point at one.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel betacoronavirus with a 30-kb genome that was first reported in December 2019 in Wuhan, China. SARS-CoV-2 was declared a public health emergency of international concern on 30 January 2020. As of 12 July 2020, coronavirus disease 2019 (COVID-19) has caused over 12.5 million cases and 561 thousand deaths globally. The virus can be classified into two main phylogenetic lineages, namely A and B, that spread from Wuhan before strict travel restrictions were enacted and now co-circulate around the world. The case fatality ratio of SARS-CoV-2 infection has been estimated between 1.2 and 1.6% with substantially higher ratios in those aged above 60 years. Some estimates suggest that 18-56% of SARS-CoV-2 transmission is from asymptomatic or pre-symptomatic individuals, complicating epidemiological assessments and public health efforts to curb the pandemic.

Challenges of real-time assessment of transmission

While the SARS-CoV-2 epidemics in several countries including China, Italy, and Spain have been brought under control through non-pharmaceutical interventions (NPIs), the number of SARS-CoV-2 cases and deaths in Brazil continues to increase (Fig. 1A). As of 12 July 2020, Brazil has now reported 1,800,827 SARS-CoV-2 cases, the second largest number in the world, and 70,398 deaths. Over a third of the cases (34%) in Brazil are concentrated in the southeast region which includes São Paulo city (Fig. 1B), the world’s fourth largest conurbation, where the first case in Latin America was reported on 25 February 2020. Diagnostic assays for SARS-CoV-2 molecular detection were widely distributed across the regional reference centres of the national public health laboratory network from 21 February 2020 onwards. However, several factors, including delays in reporting, changes in notification, and heterogeneous access to testing across populations, obfuscate the real-time assessment of virus transmission using SARS-CoV-2 case counts. Consequently, a more accurate measure of SARS-CoV-2 transmission in Brazil is provided by reported deaths due to severe acute respiratory infections (SARI), provided by the Sistema Único de Saúde (SUS). Changes in the opportunity for SARS-CoV-2 transmission are strongly associated with changes in average mobility, and can typically be measured by calculating the effective reproduction number, R, defined as the average number of secondary infections caused by an infected person. R >1 indicates a growing epidemic while R <1 is needed to achieve a decrease in transmission.

We used a Bayesian semi-mechanistic model to analyze SARI mortality statistics and human mobility data to estimate daily changes in R in São Paulo city (12,2 million inhabitants) and Rio de Janeiro city (6,7 million inhabitants), the largest urban metropoles in Brazil (Fig. 1, C and D). NPIs in Brazil consisted of school closures implemented between 12 and 23 March 2020 across the country’s 27 federal units/states, and store closures implemented between 13 and 23 March 2020. In São Paulo city, schools started closing on 16 March and stores closed four days later. At the start of the epidemics, we found R >3 in São Paulo and Rio de Janeiro, and that concurrent with the timing of state mandated NPIs, R values fell close to 1.

Mobility driven reproduction number changes

Analysis of the reproduction number after NPI implementation highlights several notable mobility-driven features. There was a period immediately following NPIs, between 21 and 31 March 2020, when R was consistently <1 in São Paulo city (Fig. 1C). However, after this initial decrease, the R value for São Paulo rises >1 and increases through time, a trend associated with increased population mobility. This can be seen in the Google transit stations index, which rises from -60% to -52%, and by a decrease in the social isolation index from 54% to 47%. By 4 May 2020, we estimate R = 1.3 (BCI 95%: 1.0-1.6) in both São Paulo and Rio de Janeiro cities (table S1). However, we note that there were instances in the previous 7 days when the 95% credible intervals for R included values below 1, drawing attention to the fluctuations and uncertainty in the estimated reproduction number for both cities.

Early sharing of genomic sequences, including the first SARS-CoV-2 genome, Wuhan-Hu-1, released on 10 January, has enabled unprecedented global levels of molecular testing for an emerging virus. However, despite the thousands of virus genomes deposited on public access databases, there is a lack of consistent sampling structure, and limited data from Brazil, which hampers accurate reconstructions of virus movement and transmission using phylogenetic analyses. To investigate how SARS-CoV-2 became established in the country, and to quantify the impact of NPIs on virus spatiotemporal spread, we tested a total of 26,732 samples from public and private laboratories using real-time PCR assays and found 7,944 (29%) to be positive for SARS-CoV-2. We then focused our sequencing efforts on generating a large and spatially representative genomic dataset with curated metadata in order to maximise the association between the number of sequences and the number of SARS-CoV-2 confirmed cases per state.

Spatially representative sequencing efforts

We generated 427 new SARS-CoV-2 genomes with >75% genome coverage from Brazilian samples collected between 5 March and 30 April 2020 (figs. S1 to S3 and data S1). For each state, the time between the date of the first reported case and the collection date of the first sequence analyzed in that state was only 4.5 days on average (Fig. 2A). For eight federal states, genomes were obtained from samples collected up to 6 days before the first case notifications. The genomes generated here were collected in 85 municipalities across 18 of 27 federal units spanning all regions in Brazil (Fig. 2A and fig. S2). Sequenced genomes were obtained from samples collected on average 4 days (median, range: 0 to 29 days) after onset of symptoms and were generated in 3 laboratories using harmonized sequencing and bioinformatic protocols (table S2). When we include 63 additional available sequences from Brazil deposited in GISAID (see data S1 and S2), we find the data set is representative of the spatial heterogeneity of the Brazilian epidemic. Specifically, the number of genomes per state strongly correlates with SARI SARS-CoV-2 confirmed cases and SARI cases with unknown aetiology per state (n = 490 sequences from 21 states, Spearman’s correlation, ρ = 0.83; Fig. 2A). This correlation varied from 0.70 to 0.83 when considering SARI cases and deaths caused by SARS-CoV-2, and SARI cases and deaths from unknown aetiology (fig. S4). Most (n = 485/490) Brazilian sequences belong to SARS-CoV-2 lineage B, with only 5 strains belonging to lineage A (2 from Amazonas, 1 from Rio Grande do Sul, 1 from Minas Gerais and 1 from Rio de Janeiro; data S1 and fig. S5 show detailed lineage information for each sequence). Moreover, we used an in-silico assessment of diagnostic assay specificity for Brazilian strains (n = 490) to identify potential mismatches in some assays targeting Brazilian strains. We find that the forward primers of the Chinese CDC and Hong Kong University nucleoprotein-targeting RT-qPCR may be less appropriate for use in Brazil than other diagnostic assays, for which few or no mismatches were identified (fig. S6 and table S3). The impact of these mismatches on the sensitivity of these assays should be confirmed experimentally. If sensitivity is affected, the use of duplex RT-qPCR assays that concurrently target different genomic regions may help the detection of viruses with variants in primer or probe binding regions.

Phylogenetic analyses and international introductions

We estimated maximum likelihood and molecular clock phylogenies for a global dataset with a total of 1,182 genomes sampled from 24 Dec 2019 to 30 Apr 2020 (root-to-tip genetic distance correlation with sampling dates, r2 = 0.53; Fig. 3A and fig. S7). We inferred a median evolutionary rate of 1.13 × 10−3 (95% BCI: 1.03–1.23 × 10−3) substitutions per site per year (s/s/y), using an exponential growth coalescent model, equating to 33 changes per year on average across the virus genome. This is within the range of evolutionary rates estimated for other human coronaviruses. We estimate the date of the common ancestor (TMRCA) of the SARS-CoV-2 pandemic to around mid-Nov 2019 (median = 19 Nov 2019, 95% BCI: 26 Oct 2019 to 6 Dec 2019), in line with recent findings.

Phylogenetic analysis revealed that the majority of the Brazilian genomes (76%, n = 370/490) fell into three clades hereafter named as Clade 1 (n = 186/490, 38% of Brazilian strains), Clade 2 (n = 166, 34%) and Clade 3 (n = 18/490, 4%; Fig. 3A and figs. S8 and S9), which were largely in agreement with those identified in a phylogenetic analysis using 13,833 global genomes. The most recent common ancestors of the three main Brazilian clades (Clades 1 to 3) were dated from 28 February (21 Feb to 4 Mar 2020) (Clade 1), 22 February (17 to 24 Feb 2020) (Clade 2) to 11 March (9 to 12 Mar 2020) (Clade 3) (Fig. 3A and fig. S10). This indicates that community-driven transmission was already established in Brazil by early March, suggesting that international travel restrictions initiated after this period would have had limited impact. Brazilian Clade 1 is characterized by a nucleotide substitution in the spike protein (G25088T, numbering relative to GenBank reference NC_045512.2) and circulates predominantly in São Paulo state (n = 159, 85.4%; figs. S9 and S11). Clade 2 is defined by two nucleotide substitutions in ORF6 (T27299C) and nucleoprotein (T29148C); this is the most spatially widespread lineage, with sequences from a total of 16 states in Brazil. Clade 3 is concentrated in Ceará state (n = 16, 89%) and falls in a global cluster with sequences mainly from Europe. In the Amazon region, where the epidemic is expanding rapidly, we find evidence for multiple national and international introductions, with 37% (n = 7/19) of sequences from Pará and Amazonas states clustering in Clade 1 and 32% (n = 6/19) in Clade 2.

Time-measured phylogeographic analyses revealed at least 102 (95% BCI: 95–109) international introductions of SARS-CoV-2 in Brazil (Fig. 3A and figs. S8 and S12). This represents an underestimate of the real number of introductions, as we have sequenced, on average, only 1 out of 200 confirmed cases. Most of these estimated introductions were directed to internationally well-connected states such as São Paulo (36% of all imports), Minas Gerais (24%), Ceará (10%) and Rio de Janeiro (8%) (fig. S12). We further assessed the contribution of international vs. national virus lineage movement events through time (Fig. 3B). In the first phase of the epidemic, we find an increasing number of international introductions until 10 Mar 2020 (Fig. 2B). Limited available travel history data suggests that these early cases were predominantly acquired from Italy (26%, n = 70 of 266 unambiguously identified country of infection) and the USA (28%, n = 76 of 266). After this initial phase, we find that the estimated number of international imports decreased concomitantly with the decline in the number of international passengers travelling to Brazil (Fig. 3, B and C, and S13). In contrast, despite the declines in the number of passengers travelling on national flights (Fig. 3C), we detected an increase in virus lineage movement events between Brazilian regions at least until early April 2020.

Modelling spatiotemporal spread within Brazil

To better understand virus spread across spatiotemporal scales within Brazil, we use a continuous phylogeographic model that maps phylogenetic nodes to their inferred origin locations (Fig. 4). We distinguish branches that remain within a state versus those that cross a state to infer the proportion of within versus between state observed virus movement.

We estimate that during the first epidemic phase, SARS-CoV-2 spread mostly locally and within-state borders. In contrast, the second phase was characterized by long-distance movement events and the ignition of the epidemic outside the southeast region of Brazil (Fig. 4A). Throughout the epidemic, we find that within-state virus lineage movement was, on average, 5.1-fold more frequent than between-state movement. Moreover, our data suggests that within-state virus spread, and to a lesser extent, between-state virus spread, decreased after the implementation of NPIs (Fig. 4B). However, it is useful to note that the more limited sampling after April 6 2020 (see fig. S2) decreases inferred virus lineage movement toward present (Figs. 3B and 4B).

Interestingly, we find that the average route length travelled by passenger increased by 25% during the second phase of the epidemic (Fig. 4C), despite a concomitant reduction in the number of passengers flying within Brazil (Fig. 3C). The increase in the average route length post-NPI implementation results from a larger reduction in the number of air passengers flying on shorter distance journeys compared to those flying longer distance journeys. For example, we find an 8.8-fold reduction in the number of passengers flying in flight legs < 1000 km, compared to a 4.4-fold reduction in those flying >2000 km (fig. S15). These findings emphasize the roles of within and between-state mobility as a key driver of both local and inter-regional virus spread, with highly populated and well-connected urban conurbations in the southeast region acting as main sources of virus exports within the country (fig. S12).

Discussion

We provide a comprehensive analysis of SARS-CoV-2 spread in Brazil that shows the importance of community and nation-wide measures to control the COVID-19 epidemic Brazil. Although NPIs initially reduced virus transmission and spread, the continued increase in the number of cases and deaths in Brazil highlights the urgent need to prevent future virus transmission by implementing rapid and accessible diagnostic screening, contact tracing, quarantining of new cases and coordinated social and physical distancing measures across the country. With the recent relaxation of NPIs in Brazil and elsewhere, continued molecular, immunological and genomic surveillance are required for real-time data-driven decisions. Our analysis shows how changes in mobility may impact global and local transmission of SARS-CoV-2, and demonstrates how combining genomic and mobility data can complement traditional surveillance approaches.

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    Thailand Ministry of Public Health, Diagnostic detection of novel coronavirus 2019 by real time RT-PCR (2020); https://www.who.int/docs/default-source/coronaviruse/conventional-rt-pcr-followed-by-sequencing-for-detection-of-ncov-rirl-nat-inst-health-t.pdf?sfvrsn = 42271c6d_4.

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    Chinese National Institute for Viral Disease Control and Prevention, Specific primers and probes for detection 2019 novel coronavirus (2020); http://ivdc.chinacdc.cn/kyjz/202001/t20200121_211337.html.

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    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); https://www.who.int/docs/default-source/coronaviruse/peiris-protocol-16-1-20.pdf?sfvrsn = af1aac73_4.

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    Huson D. H., Bryant D., Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006 ).

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  65. 65.
    Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10 · 2018
    No notice
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    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 ).

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    Bayesian Phylogeography Finds Its Roots · 2009
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  71. 71.

    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 ).

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    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 ).

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  73. 73.

    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 ).

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  74. 74.

    Minin V. N., Suchard M. A., Counting labeled transitions in continuous-time Markov models of evolution. J. Math. Biol. 56, 391–412 (2008 ).

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  75. 75.

    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 ).

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    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 ).

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    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 ).

    Not in our library10.1093/bioinformatics/btw384

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