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

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.

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.

(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 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.

(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.