COVID-19 was first detected in Brazil on March 1, 2020, making it the first Latin American country to report a case of the novel coronavirus. Since then, the number of infections has risen drastically, reaching approximately 38 million cases by May 11, 2025. Meanwhile, the first local death due to the disease was reported in March 19, 2020. Four years later, the number of fatal cases had surpassed 700,000. The highest COVID-19 death toll in Latin America With a population of more than 211 million inhabitants as of 2023, Brazil is the most populated country in Latin America. This nation is also among the most affected by COVID-19 in number of deaths, not only within the Latin American region, but also worldwide, just behind the United States. These figures have raised a debate on how the Brazilian government has dealt with the pandemic. In fact, according to a study carried out in May 2021, more than half of Brazilians surveyed disapproved of the way in which former president Jair Bolsonaro had been dealing with the health crisis. In comparison, a third of respondents had a similar opinion about the Ministry of Health. Brazil’s COVID-19 vaccination campaign rollout Brazil’s vaccination campaign started at the beginning of 2021, when a nurse from São Paulo became the first person in the country to get vaccinated against the disease. A few years later, roughly 88 percent of the Brazilian population had received at least one vaccine dose, while around 81 percent had already completed the basic immunization scheme. With more than 485.2 million vaccines administered as of March 2023, Brazil was the fourth country with the most administered doses of the COVID-19 vaccine globally, after China, India, and the United States.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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Content

From Novel Corona Virus 2019 Dataset:

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has information on the number of cases in Brazil. Please note that this is a time series data and so the number of cases on any given day is a cumulative number.

The data is available from Jan/30/2020, when the first suspect case appeared in Brazil.

Acknowledgements

1. Avisos/Advertising - Please, before start working with this data, take a break to read this discussion.
2. Plataforma COVID Brazil - Public platform to share informations about the COVID-19 cases in Brazil.

Country level datasets

If you are interested in know about another country, please follow these Kaggle datasets:

• Novel Corona Virus 2019 Dataset - https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
• India - https://www.kaggle.com/sudalairajkumar/covid19-in-india
• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland
• Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases
• Turkey - https://www.kaggle.com/gkhan496/covid19-in-turkey
• France - https://www.kaggle.com/lperez/coronavirus-france-dataset
• Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia
• Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

Inspiration

1. Changes in number of cases over time
2. Change in cases over time at state level

As of September 21, 2023, São Paulo was the Brazilian state where the majority of fatal COVID-19 cases occurred, with approximately 180,887 deaths recorded as of that day. Rio de Janeiro trailed in second, registering around 77,344 fatal cases due to the disease. As of August 2, 2023, the number of deaths from COVID-19 in Brazil reached around 704,659 people. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Brazil recorded 37511921 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Brazil reported 702116 Coronavirus Deaths. This dataset includes a chart with historical data for Brazil Coronavirus Cases.

In past 24 hours, Brazil, South America had N/A new cases, N/A deaths and N/A recoveries.

Data of COVID-19 in Brazil

Souce: https://covid.saude.gov.br/ Updated in 16 November 2020

View daily updates and historical trends for Brazil Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts analyti…

City level open access data from 26 States and the Federal District and from the Brazilian Institute of Geography and Statistics (IBGE) [20], the Department of Informatics of Brazilian Public Health System – DATASUS, Ministry of Health, the Brazilian Agricultural Research Corporation (Embrapa) and from Brazil.io. Data from all 5,570 cities in Brazil were included in the analysis. COVID-19 data included cases and deaths reported between February 26th, 2020 and February 4th, 2021. The following outcomes were computed: a) days between the first case in Brazil until the first case in the city; b) days between the first case in the city until the day when 1,000 cases were reported; and c) days between the first death in city until the day when 50 deaths inhabitants were reported. Descriptive analyses were performed on the following: proportion of cities reaching 1,000 cases; number of cases at three, six, nine and 12 months after first case; cities reporting at least one COVID-19 related death; number of COVID-19 related deaths at three, six, nine and 12 months after first death in the country. All incidence data is adjusted for 100,000 inhabitants.The following covariates were included: a) geographic region where the city is located (Midwest, North, Northeast, Southeast and South), metropolitan city (no/yes) and urban or rural; b) social and environmental city characteristics [total area (Km2), urban area (Km2), population size (inhabitants), population living within urban area (inhabitants), population older than 60 years (%), indigenous population (%), black population (%), illiterate older than 25 years (%) and city in extreme poverty (no/yes)]; c) housing conditions [household with density >2 per dormitory (%), household with garbage collection (%), household connected to the water supply system (%) and household connected to the sewer system (%)]; d) job characteristics [commerce (%) and informal workers (%)]; e) socioeconomic and inequalities characteristics [GINI index; income per capita; poor or extremely poor (%) and households in informal urban settlements (%)]; f) health services access and coverage [number of National Public Health System (SUS) physicians per inhabitants (100,000 inhabitants), number of SUS nurses per inhabitants (100,000 inhabitants), number of intensive care units or ICU per inhabitants (100,000 inhabitants). All health services access and coverage variables were standardized using z-scores, combined into one single variable categorized into tertiles.

According to a survey conducted in April 2020, 34 percent of respondents in Brazil reported to have increased the use of delivery services after the COVID-19 outbreak in their country. Moreover, 28 percent of respondents also said that they were buying more online for the same reason. In case that the pandemic lasts for the following months, nearly 40 percent of respondents would avoid purchasing in cash, while 35 percent would use delivery services more frequently than usual.

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Context - World Health Organization (WHO)

• Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus (2019-nCoV).
• At this time, there are no specific vaccines or treatments for COVID-19. However, there are many ongoing clinical trials evaluating potential treatments.
• The best way to prevent and slow down transmission is to be well informed about the COVID-19 virus, the disease it causes, and how it spreads.
• As of 28 April 2020, 71.886 cases have been confirmed in Brazil.

Content

1. This dataset has information on the number of confirmed cases, deaths, and recoveries (by neighborhood) in the city of Rio de Janeiro, Brazil.
2. Please note that this is a time-series data and so the number of cases on any given day is a cumulative number.
3. The number of new cases can be obtained by the difference between current and previous days.
4. The data is available from 21 April 2020 and the dataset is updated on a daily basis.

Acknowledgements

• Rio de Janeiro City Hall, Municipal Health Secretariat & Others - Painel COVID-19 Rio
• Raphael Fontes @unanimad - Dataset Inspiration

Inspiration

• Changes in the number of confirmed cases, deaths, and recoveries by neighborhood over time.
• Changes in the number of confirmed cases, deaths, and recoveries at the city level.
• Spread of the disease in the city.

The number of COVID-19 vaccination doses administered per 100 people in Brazil rose to 226 as of Oct 27 2023. This dataset includes a chart with historical data for Brazil Coronavirus Vaccination Rate.

COVID-19: No. of Tests: Serious Cases: New: RT-PCR Tests: by State: Southeast: São Paulo: Ignored data was reported at 0.000 Unit in 28 Mar 2025. This stayed constant from the previous number of 0.000 Unit for 27 Mar 2025. COVID-19: No. of Tests: Serious Cases: New: RT-PCR Tests: by State: Southeast: São Paulo: Ignored data is updated daily, averaging 0.000 Unit from Aug 2002 (Median) to 28 Mar 2025, with 8247 observations. The data reached an all-time high of 0.000 Unit in 28 Mar 2025 and a record low of 0.000 Unit in 28 Mar 2025. COVID-19: No. of Tests: Serious Cases: New: RT-PCR Tests: by State: Southeast: São Paulo: Ignored data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Brazil Premium Database’s Health Sector – Table BR.HLA003: Disease Outbreaks: COVID-19: Number of Tests: Serious Cases.

View daily updates and historical trends for Brazil Coronavirus Deaths Per Day. Source: Johns Hopkins Center for Systems Science and Engineering. Track ec…

Brazil recorded 16779136 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Brazil reported 617271 Coronavirus Deaths. This dataset includes a chart with historical data for Brazil Coronavirus Recovered.

ABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.

ABSTRACT Objective: To estimate the reporting rates of coronavirus disease 2019 (COVID-19) cases for Brazil as a whole and states. Methods: We estimated the actual number of COVID-19 cases using the reported number of deaths in Brazil and each state, and the expected case-fatality ratio from the World Health Organization. Brazil’s expected case-fatality ratio was also adjusted by the population’s age pyramid. Therefore, the notification rate can be defined as the number of confirmed cases (notified by the Ministry of Health) divided by the number of expected cases (estimated from the number of deaths). Results: The reporting rate for COVID-19 in Brazil was estimated at 9.2% (95%CI 8.8% - 9.5%), with all the states presenting rates below 30%. São Paulo and Rio de Janeiro, the most populated states in Brazil, showed small reporting rates (8.9% and 7.2%, respectively). The highest reporting rate occurred in Roraima (31.7%) and the lowest in Paraiba (3.4%). Conclusion: The results indicated that the reporting of confirmed cases in Brazil is much lower as compared to other countries we analyzed. Therefore, decision-makers, including the government, fail to know the actual dimension of the pandemic, which may interfere with the determination of control measures.

Dataset

This dataset was created by Luiz Fernando

Released under CC0: Public Domain

Contents

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....

This dataset contains three brazilian open COVID-19 datasets from June 1st, 2020. The Flu-Like Syndrome dataset contains several information about flu-like syndrome (Síndrome Gripal) patients. It contains mostly mild Flu-Like Syndrome cases, including COVID-19 confirmed cases. There is not an official codebook for this dataset, but portuguese speakers can easily infer the meaning of the columns.

The SARS dataset (Severe Acute Respiratory Syndrome, or Síndrome Respiratória Aguda Grave - SRAG - in portuguese) contains mostly cases where hospitalization is needed (even though some of the cases from this dataset didn't need hospitalization. These cases can be filtered using the column "HOSPITA"). Part of the patients from this dataset are confirmed COVID-19 cases (the column "CLASSI_FIN" is 5 for confirmed cases). There is an official codebook for this dataset (SARS_Codebook.pdf), but unfortunately it is written in portuguese.

The Officially Reported Cases dataset contains only confirmed COVID-19 cases that were officially reported by the government. It contains the number of cases and deaths reported until each day for each Brazilian city.

When analyzing the data, beware of notification lag: These datasets contains the cases reported until June 1st, but notified cases usually take some days to be reported. This explains the small number of cases for dates close to June 1st in the Flu-Like Syndrome and SARS datasets.

All datasets shared here are open datasets that were shared by the Brazilian Ministry of Health. The Flu-Like Syndrome and SARS datasets were downloaded from https://opendatasus.saude.gov.br, and the dataset for officially reported cases was downloaded from https://covid.saude.gov.br/. However, the Flu-Like Syndrome and the officially reported cases datasets were removed from these websites on June 7, 2020, and June 6, 2020, respectively.

Context

The original COVID-19 dataset included information about tested patients, containing early-stage symptoms, comorbidities, demographics information, and symptoms description. The patients were tested by applying viral or rapid tests. The raw data was collected by the public health agency of the city of Campina Grande, Paraíba state, in Northeast Brazil. Such a public agency is informed by all the COVID-19 exams performed in the city of Campina Grande. The health agency employees removed patient identification, and the data made available were reused to enable this study.

This dataset relates to the study entitled "Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil".

Acknowledgements

Viana dos Santos Santana, Íris ; C. M. da Silveira,, Andressa; Sobrinho, Alvaro; Chaves e Silva, Lenardo ; Dias da Silva, Leandro ; Freire de Souza Santos, Danilo ; Candeia, Edmar ; Perkusich, Angelo (2021), “A Brazilian dataset of symptomatic patients for screening the risk of COVID-19”, Mendeley Data, V5, doi: 10.17632/b7zcgmmwx4.5