Coronavirus disease (COVID-19) pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus with zoonotic origins that was first detected in China in December 2019. Brazil confirmed the first cases on February 26, 2020, in a patient returning from Italy.
Epidemiological situation in Brazil. Official data
To track the number of cases and deaths in Brazil, check out our new COVID-19 Local Information Comparison (CLIC) tool done in collaboration with the Centre for Mathematical Modelling of Infectious Diseases, from the London School of Hygiene and Tropical Medicine. Official case and death counts can be found in the Painel Coronavirus. Now including measures for the effective reproductive number in real-time for all 5568 municipalities and Federal District in Brazil.
First SARS-CoV-2 genomes in Latin America. Read more
We conducted the genome sequencing and analysis of the first confirmed COVID-19 infections in Brazil. Rapid sequencing coupled with phylogenetic analyses in the context of travel history corroborate multiple independent importations from Italy and local spread during the initial stage of COVID-19 transmission in Brazil. The first cases were sequenced and analyzed in only 48 hours.
Routes of COVID-19 introduction in Brazil. Read more
Early in March 2020, as the number of imported SARS-CoV-2 cases was on the rise in Brazil, we use incidence and historical air travel data to estimate the most important routes of importation into the country.
Between February and March 2019, Brazil received 841,302 international passengers in a total of 84 cities across the country (Figure 1). São Paulo, the largest city in the country, was the final destination of nearly half (46.1%) of the passengers arriving in Brazil, followed by Rio de Janeiro (21%) and Belo Horizonte (4.1%). More than half of the international passengers started their journey in the USA (50.8%) followed by France (7.9%) and Italy (7.5%). The air-travel routes to airports in Brazil with most passengers were USA-São Paulo (23.3%), USA-Rio de Janeiro (9.8%) and Italy-São Paulo (3.4%).
Potential for COVID-19 importation to Brazil. Panel on the left shows the proportion (%) of passengers for the top-20 routes to Brazilian airports from countries that had reported COVID-19 cases by 5th March 2020. Panel on the right side estimated proportion (%) of importations for the top-20 routes from countries that had reported local COVID-19 by 5th March 2020.
Importantly, with the recent reduction in the number of flights leaving from Italy and 51% of flights to Brazil depart from airports in the USA, we should anticipate an increasing proportion of infected travelers arriving from the USA. At a time when the number of SARS-CoV-2 cases is steadily growing in Brazil, our findings highlight the high potential for the introduction of new cases in several cities of Brazil, especially in Sao Paulo and Rio de Janeiro metropolises. Rapid identification of locations where clusters of local transmission might first ignite is critical to better coordinate preparedness, readiness, and response actions. There is a critical need for epidemiological, human mobility, and genetic data to understand virus transmission dynamics across Brazil. Continued integration of these data streams should help guide the deployment of resources to mitigate COVID-19 transmission Brazil. Check out a youtube video from Dr. Chico Camargo explaining our findings (in Portuguese).
Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil. Read more
The first case of COVID-19 was detected in Brazil on 25 February 2020. We report and contextualize epidemiological, demographic and clinical findings for COVID-19 cases during the first 3 months of the epidemic. By 31 May 2020, 514,200 COVID-19 cases, including 29,314 deaths, had been reported in 75.3% (4,196 of 5,570) of municipalities across all five administrative regions of Brazil. The R0 value for Brazil was estimated at 3.1 (95% Bayesian credible interval = 2.4–5.5), with a higher median but overlapping credible intervals compared with some other seriously affected countries. A positive association between higher per-capita income and COVID-19 diagnosis was identified. Furthermore, the severe acute respiratory infection cases with unknown aetiology were associated with lower per-capita income. Co-circulation of six respiratory viruses was detected but at very low levels. These findings provide a comprehensive description of the ongoing COVID-19 epidemic in Brazil and may help to guide subsequent measures to control virus transmission. Our study featured in Globo and Estadao.
Evolution and spread of SARS-CoV-2 in Brazil. Read more
Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February to 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 travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country. Our study featured in Jornal Nacional, G1, and CNN Brasil.
Serial Interval Distribution of SARS-CoV-2 Infection in Brazil. Read more
Current assessments of SARS-CoV-2 transmission dynamics rely on accurate estimates of key epidemiological parameters, including the serial interval, which can be defined as the time between symptom onset of the source and the onset of symptoms of the recipient. Using 65 transmission pairs of SARS-CoV-2 cases reported to the Brazilian Ministry of Health we estimated the first serial interval estimates for SARS-CoV-2 from Latin America. We estimate the mean and standard deviation for the serial interval to be 2.97 and 3.29 days respectively. We also present a model for the serial interval probability distribution using only two parameters.
Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic. Read more
The herd immunity threshold is the proportion of a population that must be immune to an infectious disease, either by natural infection or vaccination such that, in the absence of additional preventative measures, new cases decline and the effective reproduction number falls below unity. This fundamental epidemiological parameter is still unknown for the recently-emerged COVID-19, and mathematical models have predicted very divergent results. Population studies using antibody testing to infer total cumulative infections can provide empirical evidence of the level of population immunity in severely affected areas. Here we show that the transmission of SARS-CoV-2 in Manaus, located in the Brazilian Amazon, increased quickly during March and April and declined more slowly from May to September. In June, one month following the epidemic peak, 44% of the population was seropositive for SARS-CoV-2, equating to a cumulative incidence of 52%, after correcting for the false-negative rate of the antibody test. The seroprevalence fell in July and August due to antibody waning. After correcting for this, we estimate a final epidemic size of 66%. Although non-pharmaceutical interventions, plus a change in population behavior, may have helped to limit SARS-CoV-2 transmission in Manaus, the unusually high infection rate suggests that herd immunity played a significant role in determining the size of the epidemic.
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