19 datasets found
  1. COVID-19 variants in Brazil 2020-2022

    • statista.com
    Updated Nov 29, 2025
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    Statista (2025). COVID-19 variants in Brazil 2020-2022 [Dataset]. https://www.statista.com/statistics/1285473/covid-19-variants-brazil-share/
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    Dataset updated
    Nov 29, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 2020 - Jul 2022
    Area covered
    Brazil
    Description

    As of July 18, 2022, Omicron was the most prevalent variant of COVID-19 sequenced in Brazil. By that time, the share of COVID-19 cases corresponding to the Omicron BA.5 variant amounted to around 73.74 percent of the country's analyzed sequences of the SARS-CoV-2 virus. A month earlier this figure was equal to about 33 percent of the cases studied in Brazil. The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021. Since then, it has been rapidly spreading, causing an unprecedented increase in the amount of cases reported worldwide. Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

  2. COVID-19 variants in Latin America as of July 2023, by country

    • statista.com
    Updated Sep 15, 2023
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    Statista (2023). COVID-19 variants in Latin America as of July 2023, by country [Dataset]. https://www.statista.com/statistics/1284931/covid-19-variants-latin-america-selected-countries/
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    Dataset updated
    Sep 15, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Latin America
    Description

    As of July 2023, the Omicron variant was the most prevalent among selected countries in Latin America. The share of COVID-19 cases corresponding to the Omicron variant amounted to 100 percent of the analyzed sequences of SARS-CoV-2 in Colombia. The variant Omicron (XBB.1.5) accounted for nearly 81 percent of the sequenced cases in the country, while Omicron (XBB.1.9) added up to 14 percent. Similarly, Peru reported over 90 percent of its reviewed sequences corresponding to the variant Omicron (XBB.1.5), while Omicron (XBB) accounted for around 2.4 percent of cases studied. A regional overview The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021. Since then, it has been rapidly spreading, causing an unprecedented increase in the number of cases reported worldwide. In Latin America, Brazil had been the most affected country by the disease already before the emergence of the Omicron variant, with nearly 37.4 million cases and around 701,494 confirmed deaths as of May 2, 2023. However, it is Peru that has the largest mortality rate per 100,000 inhabitants due to the SARS-Cov-2 in the region, with roughly 672 deaths per 100,000 people. Vaccination campaigns in Latin America As the COVID-19 pandemic continues to cause social and economic harm worldwide, most Latin American and Caribbean countries advance their immunization programs. As of August 14, 2023, Brazil had administered the largest number of vaccines in the region, with over 486.4 million doses. Mexico and Argentina followed, with about 223.1 million and 116 million COVID-19 doses administered, respectively. However, Cuba had the highest vaccination rate not only in the region, but also the world, with around 391 vaccines given per 100 people.Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

  3. Data_Sheet_1_Transmission dynamics of SARS-CoV-2 variants in the Brazilian...

    • frontiersin.figshare.com
    • figshare.com
    xlsx
    Updated Sep 15, 2023
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    Catarina T. Pinho; Amanda F. Vidal; Tatianne Costa Negri Rocha; Renato R. M. Oliveira; Maria Clara da Costa Barros; Laura Closset; Jhully Azevedo-Pinheiro; Cíntia Braga-da-Silva; Caio Santos Silva; Leandro L. Magalhães; Pablo Diego do Carmo Pinto; Giordano Bruno Soares Souza; José Ricardo dos Santos Vieira; Rommel Mario Rodríguez Burbano; Maísa Silva de Sousa; Jorge Estefano Santana de Souza; Gisele Nunes; Moises Batista da Silva; Patrícia Fagundes da Costa; Claudio Guedes Salgado; Rita Catarina Medeiros Sousa; Wim Maurits Sylvain Degrave; Ândrea Ribeiro-dos-Santos; Guilherme Oliveira (2023). Data_Sheet_1_Transmission dynamics of SARS-CoV-2 variants in the Brazilian state of Pará.xlsx [Dataset]. http://doi.org/10.3389/fpubh.2023.1186463.s001
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    xlsxAvailable download formats
    Dataset updated
    Sep 15, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Catarina T. Pinho; Amanda F. Vidal; Tatianne Costa Negri Rocha; Renato R. M. Oliveira; Maria Clara da Costa Barros; Laura Closset; Jhully Azevedo-Pinheiro; Cíntia Braga-da-Silva; Caio Santos Silva; Leandro L. Magalhães; Pablo Diego do Carmo Pinto; Giordano Bruno Soares Souza; José Ricardo dos Santos Vieira; Rommel Mario Rodríguez Burbano; Maísa Silva de Sousa; Jorge Estefano Santana de Souza; Gisele Nunes; Moises Batista da Silva; Patrícia Fagundes da Costa; Claudio Guedes Salgado; Rita Catarina Medeiros Sousa; Wim Maurits Sylvain Degrave; Ândrea Ribeiro-dos-Santos; Guilherme Oliveira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State of Pará
    Description

    IntroductionAfter three years since the beginning of the pandemic, the new coronavirus continues to raise several questions regarding its infectious process and host response. Several mutations occurred in different regions of the SARS-CoV-2 genome, such as in the spike gene, causing the emergence of variants of concern and interest (VOCs and VOIs), of which some present higher transmissibility and virulence, especially among patients with previous comorbidities. It is essential to understand its spread dynamics to prevent and control new biological threats that may occur in the future. In this population_based retrospective observational study, we generated data and used public databases to understand SARS-CoV-2 dynamics.MethodsWe sequenced 1,003 SARS-CoV-2 genomes from naso-oropharyngeal swabs and saliva samples from Pará from May 2020 to October 2022. To gather epidemiological data from Brazil and the world, we used FIOCRUZ and GISAID databases.ResultsRegarding our samples, 496 (49.45%) were derived from female participants and 507 (50.55%) from male participants, and the average age was 43  years old. The Gamma variant presented the highest number of cases, with 290 (28.91%) cases, followed by delta with 53 (5.28%). Moreover, we found seven (0.69%) Omicron cases and 651 (64.9%) non-VOC cases. A significant association was observed between sex and the clinical condition (female, p = 8.65e-08; male, p = 0.008961) and age (p = 3.6e-10).DiscussionAlthough gamma had been officially identified only in December 2020/January 2021, we identified a gamma case from Belém (capital of Pará State) dated May 2020 and three other cases in October 2020. This indicates that this variant was circulating in the North region of Brazil several months before its formal identification and that Gamma demonstrated its actual transmission capacity only at the end of 2020. Furthermore, the public data analysis showed that SARS-CoV-2 dispersion dynamics differed in Brazil as Gamma played an important role here, while most other countries reported a new infection caused by the Delta variant. The genetic and epidemiological information of this study reinforces the relevance of having a robust genomic surveillance service that allows better management of the pandemic and that provides efficient solutions to possible new disease-causing agents.

  4. COVID-19 cases in Latin America 2025, by country

    • statista.com
    Updated Jun 5, 2025
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    Statista (2025). COVID-19 cases in Latin America 2025, by country [Dataset]. https://www.statista.com/statistics/1101643/latin-america-caribbean-coronavirus-cases/
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    Dataset updated
    Jun 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Americas, Latin America
    Description

    Brazil is the Latin American country affected the most by the COVID-19 pandemic. As of May 2025, the country had reported around 38 million cases. It was followed by Argentina, with approximately ten million confirmed cases of COVID-19. In total, the region had registered more than 83 million diagnosed patients, as well as a growing number of fatal COVID-19 cases. The research marathon Normally, the development of vaccines takes years of research and testing until options are available to the general public. However, with an alarming and threatening situation as that of the COVID-19 pandemic, scientists quickly got on board in a vaccine marathon to develop a safe and effective way to prevent and control the spread of the virus in record time. Over two years after the first cases were reported, the world had around 1,521 drugs and vaccines targeting the COVID-19 disease. As of June 2022, a total of 39 candidates were already launched and countries all over the world had started negotiations and acquisition of the vaccine, along with immunization campaigns. COVID vaccination rates in Latin America As immunization against the spread of the disease continues to progress, regional disparities in vaccination coverage persist. While Brazil, Argentina, and Mexico were among the Latin American nations with the most COVID-19 cases, those that administered the highest number of COVID-19 doses per 100 population are Cuba, Chile, and Peru. Leading the vaccination coverage in the region is the Caribbean nation, with more than 406 COVID-19 vaccines administered per every 100 inhabitants as of January 5, 2024.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  5. DATASET - The emergence of novel SARS-CoV-2 variant P.1 in Amazonas (Brazil)...

    • figshare.com
    txt
    Updated Jun 5, 2023
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    André Ricardo Ribas Freitas (2023). DATASET - The emergence of novel SARS-CoV-2 variant P.1 in Amazonas (Brazil) was temporally associated with a change in the age and sex profile of COVID-19 mortality [Dataset]. http://doi.org/10.6084/m9.figshare.14853885.v1
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    txtAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    André Ricardo Ribas Freitas
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State of Amazonas, Brazil
    Description

    This is the database of patients hospitalized with covid-19 in the state of Amazonas between March 2020 and February 2021

  6. d

    Mortality net, Mortality rate, Excess deaths and Variation of Excess deaths...

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 14, 2023
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    Grossi Morato, Eric (2023). Mortality net, Mortality rate, Excess deaths and Variation of Excess deaths in Brazil per state Jan 2014 to Aug 2021 [Dataset]. http://doi.org/10.7910/DVN/NFL2YW
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    Dataset updated
    Nov 14, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Grossi Morato, Eric
    Time period covered
    Jan 1, 2014 - Jun 30, 2021
    Area covered
    Brazil
    Description

    The excess of monthly deaths by state in Brazil, mainly in 2021, point to an unprecedented mortuary catastrophe in Brazil How has the government of Brazil acted and has acted to protect its citizens from the most important, intense and deadly event of all time, in these 521 years of Brazilian history? How great is the risk of death that its inhabitants are facing, is it possible to measure and compare with other similar human beings, but who have different governments? Can we really measure, based on scientific, safe and verified data, the performance, willingness and result of actions and even the examples that the federal government of Brazil promoted in 18 months of the years 2020 and 2021? YES, we can ! Fortunately, in this era of free and unquestionable virtual environments, it is possible to develop reliable and fast ways to search, classify, verify, index, compare and publish known health epidemiological indices of human health! The internet and the Dataverse of the Harvard School allowed, not only scientists and physicians, as any being on Earth, to consult, understand and compare results that will remain available for generations, between the past and the present, but also between countries, as in this set we deal with the safest and most important health index, we show absolute numbers of deaths and births... All the most used epidemiological variables of birth and mortality per month in Brazil, from January 2014 to June 2021, by state, country and 2 large groups of states (based on a single criterion - votes Bolsonaro 1st round 2018 > 50%) All most used epidemiological variables from mortality per month in Brazil , Jan-2015 to Jun-2021, per state and country We show the death rate, number of net deaths, excess deaths, births, birth rate, annual growth rate, growth rate variation, P-score, excess mortality rate by months by state (UF), percentage of seniors over 70 years old from January 2014 to June 2021

  7. Table_1_Early Emergence and Dispersal of Delta SARS-CoV-2 Lineage AY.99.2 in...

    • frontiersin.figshare.com
    xlsx
    Updated Jun 14, 2023
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    Camila Malta Romano; Cristina Mendes de Oliveira; Luciane Sussuchi da Silva; José Eduardo Levi (2023). Table_1_Early Emergence and Dispersal of Delta SARS-CoV-2 Lineage AY.99.2 in Brazil.XLSX [Dataset]. http://doi.org/10.3389/fmed.2022.930380.s001
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    xlsxAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Camila Malta Romano; Cristina Mendes de Oliveira; Luciane Sussuchi da Silva; José Eduardo Levi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    The year of 2021 was marked by the emergence and dispersal of a number of SARS-CoV-2 lineages, resulting in the “third wave” of COVID-19 in several countries despite the level of vaccine coverage. Soon after the first confirmed cases of COVID-19 by the Delta variant in Brazil, at least seven Delta sub-lineages emerged, including the globally spread AY.101 and AY.99.2. In this study we performed a detailed analysis of the COVID-19 scenario in Brazil from April to December 2021 by using data collected by the largest private medical diagnostic company in Latin America (Dasa), and SARS-CoV-2 genomic sequences generated by its SARS-CoV-2 genomic surveillance project (GENOV). For phylogenetic and Bayesian analysis, SARS-CoV-2 genomes available at GISAID public database were also retrieved. We confirmed that the Brazilian AY.99.2 and AY.101 were the most prevalent lineages during this period, overpassing the Gamma variant in July/August. We also estimated that AY.99.2 likely emerged a few weeks after the entry of the B.1.617.2 in the country, at some point between late April and May and rapidly spread to other countries. Despite no increased fitness described for the AY.99.2 lineage, a rapid shift in the composition of Delta SARS-CoV-2 lineages prevalence in Brazil took place. Understanding the reasons leading the AY.99.2 to become the dominant lineage in the country is important to understand the process of lineage competitions that may inform future control measures.

  8. Data from: Using data from a private provider of telemedicine to assess the...

    • scielo.figshare.com
    xls
    Updated Jun 4, 2023
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    P.M. Barbosa; F.C. da Silva Júnior; G.M.C. Lima; S. Bertini; R.R. de Lima; K.A. Furuta; C.H. Mapa; L. Roschel; E. Oliveira (2023). Using data from a private provider of telemedicine to assess the severity of the early 2021 Covid-19 wave in Brazil [Dataset]. http://doi.org/10.6084/m9.figshare.20131618.v1
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    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    P.M. Barbosa; F.C. da Silva Júnior; G.M.C. Lima; S. Bertini; R.R. de Lima; K.A. Furuta; C.H. Mapa; L. Roschel; E. Oliveira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    In early 2021, Brazil saw a dramatic recurrence in Covid-19 cases associated to the spread of a novel variant of the SARS-CoV-2 virus, the P1 variant. In light of previous reports showing that this variant is more transmissible and more likely to infect people who had recovered from previous infection, a retrospective analysis was conducted to assess if the early 2021 Covid-19 wave in Brazil was associated with an increase in the number of individuals presenting with a more severe clinical course. Fifty-one thousand and fourteen individuals who underwent telemedicine consultations were divided into two groups: patients seen on or before January 31, 2021, and on or after February 1, 2021. These dates were chosen based on the spread of the P1 variant in Brazil. Referral to the emergency department (ED) was used as a marker of a more severe course of the disease. No differences were seen in the proportion of patients referred to the ED in each group nor in the odds ratio of being referred to the ED from the 1st of February 2021 (OR=0.909; 95%CI: 0.81-1.01). Considering the entire cohort, age had an impact on the odds of being referred to the ED, with individuals older than 59 years showing twice the risk of the remaining population and those less than 19 years showing a lower risk.

  9. COVID-19 mortality rate in Latin America 2023, by country

    • statista.com
    Updated Jun 6, 2025
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    Statista (2025). COVID-19 mortality rate in Latin America 2023, by country [Dataset]. https://www.statista.com/statistics/1114603/latin-america-coronavirus-mortality-rate/
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    Dataset updated
    Jun 6, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Latin America
    Description

    Peru is the country with the highest mortality rate due to the coronavirus disease (COVID-19) in Latin America. As of November 13, 2023, the country registered over 672 deaths per 100,000 inhabitants. It was followed by Brazil, with around 331.5 fatal cases per 100,000 population. In total, over 1.76 million people have died due to COVID-19 in Latin America and the Caribbean.

    Are these figures accurate? Although countries like Brazil already rank among the countries most affected by the coronavirus disease (COVID-19), there is still room to believe that the number of cases and deaths in Latin American countries are underreported. The main reason is the relatively low number of tests performed in the region. For example, Brazil, one of the most impacted countries in the world, has performed approximately 63.7 million tests as of December 22, 2022. This compared with over one billion tests performed in the United States, approximately 909 million tests completed in India, or around 522 million tests carried out in the United Kingdom.

    Capacity to deal with the outbreak With the spread of the Omicron variant, the COVID-19 pandemic is putting health systems around the world under serious pressure. The lack of equipment to treat acute cases, for instance, is one of the problems affecting Latin American countries. In 2019, the number of ventilators in hospitals in the most affected countries ranged from 25.23 per 100,000 inhabitants in Brazil to 5.12 per 100,000 people in Peru.

    For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  10. Data from: Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro,...

    • figshare.com
    application/x-rar
    Updated Sep 13, 2022
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    Alessandra P Lamarca; Luiz G P de Almeida; Ronaldo da Silva Francisco Junior; Liliane Cavalcante; Otávio Brustolini; Alexandra L. Gerber; Ana Paula C de Guimarães; Thiago Henrique de Oliveira; Érica Ramos dos Santos Nascimento; Cíntia Policarpo; Isabelle Vasconcellos de Souza; Erika Martins de Carvalho; Mario Sergio Ribeiro; Silvia Carvalho; Flávio Dias da Silva; Marcio Henrique de Oliveira Garcia; Leandro Magalhães de Souza; Cristiane Gomes da Silva; Caio Luiz Pereira Ribeiro; Andréa Cony Cavalcanti; Claudia Maria Braga de Mello; Amilcar Tanuri; Ana Tereza R. Vasconcelos (2022). Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants. [Dataset]. http://doi.org/10.6084/m9.figshare.19125863.v1
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    application/x-rarAvailable download formats
    Dataset updated
    Sep 13, 2022
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Alessandra P Lamarca; Luiz G P de Almeida; Ronaldo da Silva Francisco Junior; Liliane Cavalcante; Otávio Brustolini; Alexandra L. Gerber; Ana Paula C de Guimarães; Thiago Henrique de Oliveira; Érica Ramos dos Santos Nascimento; Cíntia Policarpo; Isabelle Vasconcellos de Souza; Erika Martins de Carvalho; Mario Sergio Ribeiro; Silvia Carvalho; Flávio Dias da Silva; Marcio Henrique de Oliveira Garcia; Leandro Magalhães de Souza; Cristiane Gomes da Silva; Caio Luiz Pereira Ribeiro; Andréa Cony Cavalcanti; Claudia Maria Braga de Mello; Amilcar Tanuri; Ana Tereza R. Vasconcelos
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Rio de Janeiro, Brazil
    Description

    These are the supplementary files and scripts used in the the manuscript "Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants"

    Abstract
    During the first semester of 2021, all of Brazil has suffered an intense wave of COVID-19 associated with the Gamma variant. In July, the first cases of Delta variant were detected in the state of Rio de Janeiro. In this work, we have employed phylodynamic methods to analyze more than 1,600 genomic sequences of Delta variant collected until September in Rio de Janeiro to reconstruct how this variant has surpassed Gamma and dispersed throughout the state. After the introduction of Delta, it has initially spread mostly in the homonymous city of Rio de Janeiro, the most populous of the state. In a second stage, dispersal occurred to mid- and long-range cities, which acted as new close-range hubs for spread. We observed that the substitution of Gamma by Delta was possibly caused by its higher viral load, a proxy for transmissibility. This variant turnover prompted a new surge in cases, but with lower lethality than was observed during the peak caused by Gamma. We reason that high vaccination rates in the state of Rio de Janeiro were possibly what prevented a higher number of deaths.

  11. Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2,...

    • statista.com
    + more versions
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    Statista, Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2, 2023 [Dataset]. https://www.statista.com/statistics/1087466/covid19-cases-recoveries-deaths-worldwide/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2, 2023
    Area covered
    Worldwide
    Description

    As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

    The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

    Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

  12. Data_Sheet_2_Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests...

    • frontiersin.figshare.com
    pdf
    Updated Jun 21, 2023
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    Camila P. Perico; Camilla R. De Pierri; Giuseppe Pasqualato Neto; Danrley R. Fernandes; Fabio O. Pedrosa; Emanuel M. de Souza; Roberto T. Raittz (2023). Data_Sheet_2_Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin.PDF [Dataset]. http://doi.org/10.3389/fmicb.2022.1037455.s002
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    pdfAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Camila P. Perico; Camilla R. De Pierri; Giuseppe Pasqualato Neto; Danrley R. Fernandes; Fabio O. Pedrosa; Emanuel M. de Souza; Roberto T. Raittz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    Brazil was the epicenter of worldwide pandemics at the peak of its second wave. The genomic/proteomic perspective of the COVID-19 pandemic in Brazil could provide insights to understand the global pandemics behavior. In this study, we track SARS-CoV-2 molecular information in Brazil using real-time bioinformatics and data science strategies to provide a comparative and evolutive panorama of the lineages in the country. SWeeP vectors represented the Brazilian and worldwide genomic/proteomic data from Global Initiative on Sharing Avian Influenza Data (GISAID) between February 2020 and August 2021. Clusters were analyzed and compared with PANGO lineages. Hierarchical clustering provided phylogenetic and evolutionary analyses of the lineages, and we tracked the P.1 (Gamma) variant origin. The genomic diversity based on Chao's estimation allowed us to compare richness and coverage among Brazilian states and other representative countries. We found that epidemics in Brazil occurred in two moments with different genetic profiles. The P.1 lineages emerged in the second wave, which was more aggressive. We could not trace the origin of P.1 from the variants present in Brazil. Instead, we found evidence pointing to its external source and a possible recombinant event that may relate P.1 to a B.1.1.28 variant subset. We discussed the potential application of the pipeline for emerging variants detection and the PANGO terminology stability over time. The diversity analysis showed that the low coverage and unbalanced sequencing among states in Brazil could have allowed the silent entry and dissemination of P.1 and other dangerous variants. This study may help to understand the development and consequences of variants of concern (VOC) entry.

  13. f

    Data_Sheet_3_Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests...

    • frontiersin.figshare.com
    pdf
    Updated Jun 21, 2023
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    Camila P. Perico; Camilla R. De Pierri; Giuseppe Pasqualato Neto; Danrley R. Fernandes; Fabio O. Pedrosa; Emanuel M. de Souza; Roberto T. Raittz (2023). Data_Sheet_3_Genomic landscape of the SARS-CoV-2 pandemic in Brazil suggests an external P.1 variant origin.PDF [Dataset]. http://doi.org/10.3389/fmicb.2022.1037455.s003
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    pdfAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers
    Authors
    Camila P. Perico; Camilla R. De Pierri; Giuseppe Pasqualato Neto; Danrley R. Fernandes; Fabio O. Pedrosa; Emanuel M. de Souza; Roberto T. Raittz
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    Brazil was the epicenter of worldwide pandemics at the peak of its second wave. The genomic/proteomic perspective of the COVID-19 pandemic in Brazil could provide insights to understand the global pandemics behavior. In this study, we track SARS-CoV-2 molecular information in Brazil using real-time bioinformatics and data science strategies to provide a comparative and evolutive panorama of the lineages in the country. SWeeP vectors represented the Brazilian and worldwide genomic/proteomic data from Global Initiative on Sharing Avian Influenza Data (GISAID) between February 2020 and August 2021. Clusters were analyzed and compared with PANGO lineages. Hierarchical clustering provided phylogenetic and evolutionary analyses of the lineages, and we tracked the P.1 (Gamma) variant origin. The genomic diversity based on Chao's estimation allowed us to compare richness and coverage among Brazilian states and other representative countries. We found that epidemics in Brazil occurred in two moments with different genetic profiles. The P.1 lineages emerged in the second wave, which was more aggressive. We could not trace the origin of P.1 from the variants present in Brazil. Instead, we found evidence pointing to its external source and a possible recombinant event that may relate P.1 to a B.1.1.28 variant subset. We discussed the potential application of the pipeline for emerging variants detection and the PANGO terminology stability over time. The diversity analysis showed that the low coverage and unbalanced sequencing among states in Brazil could have allowed the silent entry and dissemination of P.1 and other dangerous variants. This study may help to understand the development and consequences of variants of concern (VOC) entry.

  14. Table 1_SARS-CoV-2 strains and clinical profiles of COVID-19 patients in a...

    • frontiersin.figshare.com
    pptx
    Updated Dec 18, 2024
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    Bibiana S. de Oliveira Fam; Nathan Araujo Cadore; Renan Sbruzzi; Marilea Furtado Feira; Giovanna Câmara Giudicelli; Luiz G. P. de Almeida; Alexandra L. Gerber; Ana Paula de C. Guimarães; Ana Tereza Ribeiro Vasconcelos; Alexandre C. Pereira; Lygia V. Pereira; Tábita Hünemeier; Suzi Alves Camey; Fernanda S. Luiz Vianna (2024). Table 1_SARS-CoV-2 strains and clinical profiles of COVID-19 patients in a Southern Brazil hospital.docx [Dataset]. http://doi.org/10.3389/fimmu.2024.1444620.s001
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    Dataset updated
    Dec 18, 2024
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Bibiana S. de Oliveira Fam; Nathan Araujo Cadore; Renan Sbruzzi; Marilea Furtado Feira; Giovanna Câmara Giudicelli; Luiz G. P. de Almeida; Alexandra L. Gerber; Ana Paula de C. Guimarães; Ana Tereza Ribeiro Vasconcelos; Alexandre C. Pereira; Lygia V. Pereira; Tábita Hünemeier; Suzi Alves Camey; Fernanda S. Luiz Vianna
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    IntroductionThe COVID-19 pandemic had a widespread global impact and presented numerous challenges. The emergence of SARS-CoV-2 variants has changed transmission rates and immune evasion, possibly impacting the severity. This study aims to investigate the impact of variants on clinical outcomes in southern Brazil.MethodsIn total, samples from 277 patients, hospitalized and non-hospitalized, were collected between March 2020 and March 2021, before the vaccine was made widely available to the general population in Brazil. Whole genome sequencing of SARS-CoV-2 was performed and bioinformatics and biostatistics analyses were implemented on molecular and clinical data, respectively.ResultsThe study identified significant demographic and clinical differences. The hospitalized group exhibited a higher proportion of males (51.9%) and an increased prevalence of comorbidities, including hypertension (66.0%), obesity (42.6%), and chronic kidney disease (23.6%). Patients were identified with twelve SARS-CoV-2 strains, predominantly B.1.1.28 and B.1.1.33 in the early 2020 first wave, and P.1 overlapping in the late 2020 and early 2021 second wave of COVID-19. Significant differences in hospitalization rates were found among patients infected with the different SARS-CoV-2 lineages: B.1.1.33 (46.0%), B.1.1.28 (65.9%), and P.1 (97.9%). Severity markers, such as pneumonia (62.5%, p=0.002), acute respiratory distress syndrome (ARDS, 72.9%, p6 L/min O2 (64.6%, p

  15. Results for Covid-19 simulation with data from Brazil, Spain, United...

    • plos.figshare.com
    xls
    Updated Jun 17, 2024
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    Hélder Seixas Lima; Unaí Tupinambás; Frederico Gadelha Guimarães (2024). Results for Covid-19 simulation with data from Brazil, Spain, United Kingdom, and United States. [Dataset]. http://doi.org/10.1371/journal.pone.0305522.t003
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    Dataset updated
    Jun 17, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Hélder Seixas Lima; Unaí Tupinambás; Frederico Gadelha Guimarães
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Spain, United Kingdom, United States, Brazil
    Description

    Results for Covid-19 simulation with data from Brazil, Spain, United Kingdom, and United States.

  16. Summary of Covid-19 data in Brazil (2020–2022).

    • figshare.com
    xls
    Updated Jun 17, 2024
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    Hélder Seixas Lima; Unaí Tupinambás; Frederico Gadelha Guimarães (2024). Summary of Covid-19 data in Brazil (2020–2022). [Dataset]. http://doi.org/10.1371/journal.pone.0305522.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 17, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Hélder Seixas Lima; Unaí Tupinambás; Frederico Gadelha Guimarães
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    Our study conducts a comprehensive analysis of the Covid-19 pandemic in Brazil, spanning five waves over three years. We employed a novel Susceptible-Infected-Recovered-Dead-Susceptible (SIRDS) model with a fuzzy transition between epidemic periods to estimate time-varying parameters and evaluate case underreporting. The initial basic reproduction number (R0) is identified at 2.44 (95% Confidence Interval (CI): 2.42–2.46), decreasing to 1.00 (95% CI: 0.99–1.01) during the first wave. The model estimates an underreporting factor of 12.9 (95% CI: 12.5–13.2) more infections than officially reported by Brazilian health authorities, with an increasing factor of 5.8 (95% CI: 5.2–6.4), 12.9 (95% CI: 12.5–13.3), and 16.8 (95% CI: 15.8–17.5) in 2020, 2021, and 2022 respectively. Additionally, the Infection Fatality Rate (IFR) is initially 0.88% (95% CI: 0.81%–0.94%) during the initial phase but consistently reduces across subsequent outbreaks, reaching its lowest value of 0.018% (95% CI: 0.011–0.033) in the last outbreak. Regarding the immunity period, the observed uncertainty and low sensitivity indicate that inferring this parameter is particularly challenging. Brazil successfully reduced R0 during the first wave, coinciding with decreased human mobility. Ineffective public health measures during the second wave resulted in the highest mortality rates within the studied period. We attribute lower mortality rates in 2022 to increased vaccination coverage and the lower lethality of the Omicron variant. We demonstrate the model generalization by its application to other countries. Comparative analyses with serological research further validate the accuracy of the model. In forecasting analysis, our model provides reasonable outbreak predictions. In conclusion, our study provides a nuanced understanding of the Covid-19 pandemic in Brazil, employing a novel epidemiological model. The findings contribute to the broader discourse on pandemic dynamics, underreporting, and the effectiveness of health interventions.

  17. f

    DataSheet_1_The seroconversion history to SARS-CoV-2 in Indigenous people...

    • figshare.com
    pdf
    Updated Jul 16, 2024
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    Alice Nagai; Renan Barbosa Lemes; José Geraldo Mill; Alexandre Costa Pereira; Rafael Elias Marques; Tábita Hünemeier (2024). DataSheet_1_The seroconversion history to SARS-CoV-2 in Indigenous people from Brazil – the interplay between exposure, vaccination, and tuberculosis.pdf [Dataset]. http://doi.org/10.3389/fimmu.2024.1359066.s001
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    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Frontiers
    Authors
    Alice Nagai; Renan Barbosa Lemes; José Geraldo Mill; Alexandre Costa Pereira; Rafael Elias Marques; Tábita Hünemeier
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil
    Description

    The COVID-19 pandemic caused a significant loss of human lives and a worldwide decline in quality of life. Although our understanding of the pandemic has improved significantly since the beginning, the natural history of COVID-19 and its impacts on under-represented populations, such as Indigenous people from America, remain largely unknown. We performed a retrospective serological survey with two Brazilian Indigenous populations (n=624), Tupiniquim and Guarani-Mbyá. Samples were collected between September 2020 and July 2021: a period comprising the dissemination of SARS-CoV-2 variants and the beginning of COVID-19 vaccination in Brazil. Seroconversions against S and N antigens were assessed using three different commercially available ELISA kits. Samples were also used to assess the prevalence of tuberculosis (TB) in the same population (n=529). Seroconversion against SARS-CoV-2 antigens was considered positive if at least one of the three ELISA kits detected levels of specific antibodies above the threshold specified by the manufacturer. In this sense, we report 56.0% (n=349/623) of seroconverted individuals. Relative seroconversion peaked after introduction of the Coronavac vaccine in February 2021. Vaccination increased the production of anti-S IgG from 3.9% to 48.6%. Our results also indicated that 11.0% (n=46/417) of all individuals were positive for TB. Seroconversion to SARS-CoV-2 was similar between individuals with positive tuberculosis test results to those with negative test results. Most vaccinated individuals seroconverted to SARS-CoV-2, indicating that Coronavac may be as protective in individuals from these indigenous groups as observed in the general Brazilian population. COVID-19 severity was minimal regardless of incomplete vaccine coverage, suggesting that vaccination may not be the only factor protecting individuals from severe COVID-19. Tuberculosis is highly prevalent and not associated with increased seroconversion to SARS-CoV-2.

  18. f

    Comparative summary of BRICS statistics relating to the COVID-19 pandemic.

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Dec 20, 2024
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    Stephanie van Wyk; Monika Moir; Anindita Banerjee; Georgii A. Bazykin; Nidhan K. Biswas; Nikita Sitharam; Saumitra Das; Wentai Ma; Arindam Maitra; Anup Mazumder; Wasim Abdool Karim; Alessandra Pavan Lamarca; Mingkun Li; Elena Nabieva; Houriiyah Tegally; James Emmanuel San; Ana Tereza R. Vasconcelos; Joicymara S. Xavier; Eduan Wilkinson; Tulio de Oliveira (2024). Comparative summary of BRICS statistics relating to the COVID-19 pandemic. [Dataset]. http://doi.org/10.1371/journal.pgph.0003023.t001
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    Dataset updated
    Dec 20, 2024
    Dataset provided by
    PLOS Global Public Health
    Authors
    Stephanie van Wyk; Monika Moir; Anindita Banerjee; Georgii A. Bazykin; Nidhan K. Biswas; Nikita Sitharam; Saumitra Das; Wentai Ma; Arindam Maitra; Anup Mazumder; Wasim Abdool Karim; Alessandra Pavan Lamarca; Mingkun Li; Elena Nabieva; Houriiyah Tegally; James Emmanuel San; Ana Tereza R. Vasconcelos; Joicymara S. Xavier; Eduan Wilkinson; Tulio de Oliveira
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data below includes the total number of COVID-19 cases and deaths per million1,2.

  19. Profile COVID-19 Infections and Reinfections. Espírito Santo, Brazil,...

    • plos.figshare.com
    xls
    Updated Sep 10, 2025
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    Cristiano Soares da Silva Dell’Antonio; Ana Luiza Bierrenbach (2025). Profile COVID-19 Infections and Reinfections. Espírito Santo, Brazil, September 2020 to February 2023. [Dataset]. http://doi.org/10.1371/journal.pone.0331771.t001
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    Dataset updated
    Sep 10, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Cristiano Soares da Silva Dell’Antonio; Ana Luiza Bierrenbach
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    State of Espírito Santo, Brazil
    Description

    Profile COVID-19 Infections and Reinfections. Espírito Santo, Brazil, September 2020 to February 2023.

  20. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Statista (2025). COVID-19 variants in Brazil 2020-2022 [Dataset]. https://www.statista.com/statistics/1285473/covid-19-variants-brazil-share/
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COVID-19 variants in Brazil 2020-2022

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Dataset updated
Nov 29, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Dec 2020 - Jul 2022
Area covered
Brazil
Description

As of July 18, 2022, Omicron was the most prevalent variant of COVID-19 sequenced in Brazil. By that time, the share of COVID-19 cases corresponding to the Omicron BA.5 variant amounted to around 73.74 percent of the country's analyzed sequences of the SARS-CoV-2 virus. A month earlier this figure was equal to about 33 percent of the cases studied in Brazil. The Omicron variant of SARS-CoV-2 - the virus causing COVID-19 - was designated as a variant of concern by the World Health Organization in November 2021. Since then, it has been rapidly spreading, causing an unprecedented increase in the amount of cases reported worldwide. Find the most up-to-date information about the coronavirus pandemic in the world under Statista’s COVID-19 facts and figures site.

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