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.

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

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.

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.

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.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

MDS: COVID-19: No. of Deaths: To Date: Northeast: Piauí data was reported at 8,449.000 Person in 03 May 2025. This stayed constant from the previous number of 8,449.000 Person for 02 May 2025. MDS: COVID-19: No. of Deaths: To Date: Northeast: Piauí data is updated daily, averaging 7,954.000 Person from Feb 2020 (Median) to 03 May 2025, with 1895 observations. The data reached an all-time high of 8,449.000 Person in 03 May 2025 and a record low of 0.000 Person in 27 Mar 2020. MDS: COVID-19: No. of Deaths: To Date: Northeast: Piauí data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA004: Disease Outbreaks: COVID-19: No of Deaths. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

As of May 11, 2025, nearly 1.8 million people have died due COVID-19 in Latin America and the Caribbean. The country with the highest number was Brazil, reporting around 700,000 deaths. As a result of the pandemic, Brazil's GDP was forecast to decline by approximately six percent in 2020. Meanwhile, Mexico ranked second in number of deaths, with approximately 335 thousand occurrences. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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

The COVID-19 pandemic brings to light the reality of the Brazilian health system. The underreporting of COVID-19 deaths in the state of Minas Gerais (MG), where the second largest population of the country is concentrated, reveals government unpreparedness, as there is a low capacity of testing in the population, which prevents the real understanding of the general panorama of SARS-CoV-2 dissemination. The goals of this research are to analyze the causes of deaths in different Brazilian government databases (Civil Registry Transparency Portal and InfoGripe) and to assess whether there are sub-records showing an unexpected increase in the frequency of deaths from causes clinically similar to COVID-19. A descriptive and quantitative analysis of the number of deaths by COVID-19 and similar causes was performed in different databases. Our results demonstrate that different official sources had a discrepancy of 109.45% between these data referring to the same period. There was also a 758.57% increase in SARI deaths in 2020, when compared to the average of previous years. Finally, it was shown that there was an increase in the rate of pneumonia and respiratory insufficiency (RI) by 6.34 and 6.25%, respectively. In conclusion, there is an underreporting of COVID-19 deaths in MG due to the unexplained excess of deaths caused by SARI, respiratory insufficiency, and pneumonia compared to previous years.

This dataset comprises data on new and accumulated confirmed cases and death episodes for each Brazilian municipality, by epidemiological week.

Criteria used for confirmed cases (mild and moderate cases): * Laboratory * Clinical epidemiological * Clinical criterion * Clinical image Death episodes refer to COVID-19 confirmed cases that progressed to death. Reference date for cases: * symptom onset date (preferably) * notification or testing date (for missing data) Reference date for deaths: * death or case closing date * notification or testing date (for missing data) Age groups follow a five-year window. Phase and peak variables according to the epidemiological week in which the cases and deaths occurred.

This dataset was used as part project - Evaluating Effects of Social Inequalities on the COVID-19 Pandemic in Brazil. Maria Yury Ichihara and colleagues at the Centre for Data and Knowledge Integration for Health (Cidacs) at Fiocruz in Brazil created a social disparities index to measure inequalities relevant to the COVID-19 pandemic, such as unequal access to healthcare, to identify regions that are more vulnerable to infection and to better focus prevention efforts.

In Brazil, markers of inequality are associated with COVID-19 morbidity and mortality. They developed the index with available COVID-19 surveillance data, hosted on the Cidacs platform, and built a public data visualisation dashboard to share the index and patterns of COVID-19 incidence and mortality with the broader community. This enabled health managers and policymakers to monitor the pandemic situation in the most vulnerable populations and target social and health interventions.

Permissions to use this dataset must be obtained from the Ministry of Health Brazil.

A preferência eleitoral por Bolsonaro no 1º turno de 2018 por estado, mostra-se um forte valor preditivo da quantidade de mortes por COVID-19, excesso de mortes por 100000 e intensidade na redução do crescimento populacional brasileiro no 1º trimestre de 2021

The coronavirus pandemic is the biggest cause of mortality in Brazil in 2021, according to DATASUS, 21.42% of patients admitted to any hospital, whether CTI or not, die due to this infection. Being caused by a respiratory virus, where transmission only occurs if, sine qua non, contact occurs between two or more human beings and exchange of saliva and sputum particles between them. It is clear and clear that, among the few but effective measures to reduce contagion and, consequently, deaths, is the orientation to use facial protection and to reduce or, if possible, end contact between people who have not been vaccinated or who have not had COVID in the last 6 months. It is also notorious that these basic and scientifically proven health concepts are not presented with a concept of truth for the President of our country, and probably for a large part of his supporters, but how can this be demonstrated? How much does example and information matter in a global pandemic that is known to be transmissible through contact and agglomeration? It is these answers that we seek through the exposure and comparison between numerical data from the states of the federation and their relationship with the election of the 1st round of 2018 and the demographic data of mortality, birth rate, excess of deaths, mortality by COVID and others in the first quarter of 2021

A pandemia de coronavirus é a maior causa de mortalidade no Brasil em 2021. Segundo o painel de registro civil brasileiro, somente de janeiro a março de 2021 foram registrados 441007 óbitos, enquanto no mesmo período de 2019 este número foi de 297952, um excesso de mortes de 143055, o que corresponde a um aumento de 48%, muito além do esperado de +- 5%. Ainda segundo o DATASUS, 21,42% dos pacientes internados com diagnóstico de COVID-19, sendo em CTI ou não, veem a falecer devido a esta infecção. A COVID-19, infecção sistêmica causada por um virus respiratório, onde a transmissão somente ocorre se , sine qua non, ocorrer contato entre dois ou mais seres humanos e troca de partículas de saliva e escarro entre eles, por via aérea ou por mucosas e superfícies contaminadas, É nítido e claro, que entre as poucas, mas eficazes medidas para diminuição do contágio e consequentemente das mortes, estão orientações de comportamento, como usar proteção facial e diminuir, ou se possível, findar o contato entre pessoas não vacinadas ou que não tiveram COVID nos últimos 6 meses. Estes conceitos sanitários básicos e cientificamente comprovados não são brindados como conceito de verdade para o Presidente de nosso país, e provavelmente para uma grande parte de seus apoiadores e eleitores, mas como demonstrar isso? Quanto se combate um vírus desconhecido e sem tratamento curativo eficaz, o exemplo, a informação padronizada e as práticas de distanciamento social são primordiais e essenciais para o êxito no achatamento da curva de transmissão e na proteção dos indivíduos mais predispostos a complicações e principalmente a morte ?

São estas respostas que procuramos através da exposição e comparação entre dados numéricos dos estados da federação e suas relações com a eleição do 1º turno de 2018 e os dados demográficos de mortalidade, natalidade, excesso de mortes, mortalidade por COVID e outros no 1º trimestre de 2021 Este banco de dados mostra com dados do registro civil, CONASS, MS e TSE que existe estreita relação ( estatisticamente significante p=0,000845 e RR=725) entre o excesso de mortes e sua intensidade com a votação no candidato 17 - Bolsonaro no 1º turno da eleição de 2018. Dos 15 piores estados em se tratando de excesso de óbitos por 100000 entre 1ºtrimestre de 2021/2019, 100% deles Bolsonaro foi o vencedor do pleito e em somente 2 não alcançou mais de 52,5% dos votos.

Abstract Objective: to analyze confirmed cases and deaths by COVID-19 among nursing professionals in Brazil. Methods: epidemiological study using geoprocessing techniques. Data from March 20 until May 28 2020 were collected from the Conselho Federal de Enfermagem [Brazilian Federal Nursing Council]. We used Chi-squared, Mantel-Haenszel, and G test for analysing the association between deaths and age group, sex, geographical region of work. Results: 17,414 suspicious cases, 5,732 confirmed cases and 134 deaths occurred in the period. The Southeast region showed the highest number of cases (46.35%) and deaths (44.78%). The most affected age group regarding cases was 31-40 years (n = 2,515), and regarding deaths, 41-50 (n = 38). The death rate was higher in men. The variables “age group”, “sex” and “geographical region of work” were significantly correlated to deaths by COVID-19 (p < 0.05). The states Amapá, Roraima and Bahia presented the highest rate of cases per 1,000 officially acknowledged nursing professionals (6.28, 6.10 and 5.99, respectively). Conclusion: the data indicate the need for a critical perspective on the nursing field in order to combat COVID-19.

ObjectiveThis study aimed to analyze the chain of events and contributing causes associated with COVID-19 adult mortality (30–69 years old), based on qualified data on CoD from three Brazilian capitals cities, Belo Horizonte, Salvador, and Natal, in 2020.MethodsData of all deaths among residents in the three capitals in 2020 were provided by these municipalities' routine Mortality Information System (SIM). Mentions B34.2 with the markers U07.1 and U07.2 in the death certificate identified COVID-19 deaths. We used a multiple-cause-of-death approach better to understand the complexity of the morbid process of COVID-19. Conditions that appeared more frequently in the same line or above the COVID-19 mentions in the death certificate were considered a chain-of-event. Conditions that occurred more often after the codes for COVID-19 were considered as contributing.ResultsIn 2020, 7,029 records from COVID-19 as the underlying cause of death were registered in SIM in the three capitals. Among these, 2,921 (41.6%) were deceased between 30 and 69 years old, representing 17.0% of deaths in this age group. As chain-of-events, the most frequent conditions mentioned were sepsis (33.4%), SARS (32.0%), acute respiratory failure (31.9%), unspecified lower respiratory infections (unspecified pneumonia) (20.1%), and other specified respiratory disorders (14.1%). Hypertension (33.3%), diabetes unspecified type (21.7%), renal failure (12.7%), obesity (9.8%), other chronic kidney diseases (4.9%), and diabetes mellitus type 2 (4.7%) were the most frequent contributing conditions. On average, 3.04 conditions were mentioned in the death certificate besides COVID-19. This average varied according to age, place of death, and capital.ConclusionThe multiple-cause analysis is a powerful tool to better understand the morbid process due to COVID-19 and highlight the importance of chronic non-communicable diseases as contributing conditions.

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.

Brazil MDS: COVID-19: No. of Deaths: To Date data was reported at 716,075.000 Person in 26 Apr 2025. This records an increase from the previous number of 716,029.000 Person for 25 Apr 2025. Brazil MDS: COVID-19: No. of Deaths: To Date data is updated daily, averaging 685,820.000 Person from Feb 2020 (Median) to 26 Apr 2025, with 1888 observations. The data reached an all-time high of 716,075.000 Person in 26 Apr 2025 and a record low of 0.000 Person in 16 Mar 2020. Brazil MDS: COVID-19: No. of Deaths: To Date data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA004: Disease Outbreaks: COVID-19: No of Deaths. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

Dataset

This dataset was created by Luiz Fernando

Released under CC0: Public Domain

Contents

Abstract This paper aims to assess the magnitude and profile of deaths from post-COVID conditions in Brazil. Descriptive study based on preliminary data from the 2021 Mortality Information System. Records with ICD code B94.8 as the Basic Cause and with code U09 in some lines of part I or II of the declaration were considered for analysis. The distribution of deaths by geographic region, semester of occurrence, sex, age group, ethnicity/skin color, schooling, and place of occurrence was evaluated. We identified 2,948 deaths from conditions subsequent to COVID-19 were recorded, ranging from 0.5 deaths per 1,000 records in the Northeast Region to 3.6/1,000 in the Midwest Region. More than half occurred among males (58.0%), those aged 60 years or older (66.9%), and whites (51.8%). Conclusion: Deaths from post-COVID conditions had distinct sociodemographic characteristics between regions.

How did Brazil become a global epicenter of the outbreak? After seeming to ease, is the virus making a comeback?

A world leader in infections and deaths.

Latin America became an epicenter of the coronavirus pandemic in May, driven by Brazil’s ballooning caseload. Ten months after its first known case, Brazil has had more than 7.9 million cases and over 200,000 deaths.

In early June, Brazil began averaging about 1,000 deaths per day from Covid-19, joining the United States — and later India — as the countries with the world’s largest death tolls.

This dataset contains information about COVID-19 in Brazil extracted on the date 16/06/2021. It is the most updated dataset available about Covid in Brazil

Features:

🔍 date: date that the data was collected. format YYYY-MM-DD.
🔍 state: Abbreviation for States. Example: SP
🔍 city: Name of the city (if the value is NaN, they are referring to the State, not the city)
🔍 place_type: Can be City or State
🔍 order_for_place: Number that identifies the registering order for this location. The line that refers to the first log is going to be shown as 1, and the following information will start the count as an index.
🔍 is_last: Show if the line was the last update from that place, can be True or False
🔍 city_ibge_code: IBGE Code from the location
🔍confirmed: Number of confirmed cases.
🔍deaths: Number of deaths.
🔍estimated_population: Estimated population for this city/state in 2020. Data from IBGE
🔍estimated_population_2019: Estimated population for this city/state in 2019. Data from IBGE.
🔍confirmed_per_100k_inhabitants: Number of confirmed cases per 100.000 habitants (based on estimated_population).
🔍death_rate: Death rate (deaths / confirmed cases).

Acknowledgements

This dataset was downloaded from the URL bello. Thanks, Brasil.IO! Their main goal is to make all Brazilian data available to the public DATASET URL: https://brasil.io/dataset/covid19/files/ Cities map file https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/Brasil/BR/

Similar Datasets

COVID-19 - https://www.kaggle.com/rafaelherrero/covid19-brazil-full-cases-17062021 COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019 Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

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....