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.
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.
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reference: https://www.seade.gov.br/coronavirus/
Understand the progression of the virus in the state of Sao Paulo - Brazil
As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.
COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.
Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.
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Because of growing inequalities, more than one-third of the worldwide population is expected to live in slums by 2050. Although slum dwellers are at increased risk of infectious diseases, this population may have been overlooked with respect to the sustainability of virus evolution. In this study, we aimed to analyze the genetic diversity and evolution of SARS-CoV-2 in the Complexo de Favelas da Maré slum, Rio de Janeiro, Brazil, and assess its impact on the global spread of the virus. We found that this slum harbored multiple strains of SARS-CoV-2, and its amplification and genetic diversity connected with the global circulation from 2020 to 2022. Thus, enhancing surveillance in slums could be important for future epidemic/pandemic preparedness by connecting virus genetic diversity in this region with its circulation at divergent locations.
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Here we investigated whether the dengue fever pandemic of 2019-2020 may have influenced COVID-19 incidence and spread around the world. In Brazil, the geographic distribution of dengue fever was highly complementary to that of COVID-19. This was accompanied by an inverse correlation between COVID-19 and dengue fever incidence that could not be explained by socioeconomic factors. This inverse correlation was observed for 5,016 Brazilian municipalities reporting COVID-19 cases, 558 micro- and 137 meso-regions, 27 states and 5 regions. Brazilian states with high population levels of dengue IgM in 2020 exhibited: (i) lower COVID-19 case and death incidence, (ii) slower infection growth rates, and (iii) took longer to accumulate COVID-19 cases. No such inverse correlations were observed for the chikungunya virus, which is also transmitted by the Aedes aegypti mosquito. The same inverse correlation between COVID-19 and dengue fever incidence was observed for 145 locations (66 countries and the 64 states of Mexico and Colombia) in Latin America, the Caribbean, and Asia. Countries with high dengue incidence took longer to accumulate COVID-19 cases than those without dengue. Although the dataset considered has quality and availability limitations, these findings raise the possibility of an immunological cross-reaction between dengue virus serotypes and SARS-CoV-2, which could have led to partial immunological protection for COVID-19 in dengue infected communities. However, further studies are necessary to better test this hypothesis. Methods COVID-19 incidence in Brazil was obtained from Brasil.io (https://brasil.io/covid19/), which compiles data from all the Brazilian state health agencies and was accessed on 2020-10-06. The period considered in the analysis was from the first COVID-19 case to the 26th epidemiological week of 2020 (which ends on the 27 th of June 2020). The COVID-19 incidence in countries around the world was collected from Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://coronavirus.jhu.edu/).
State-level data were considered for Colombia and Mexico, and a country-level was considered for the other countries under investigation. Dengue epidemiological and serological data was compiled from data published regularly in the official epidemiological bulletins during 2019 and 2020 by the Brazilian Ministry of Health (Ministério da Saúde, 2020a and 2020b). The incidence available via DATASUS (2020) considered the period from the 27th epidemiological week of 2019 to the 26th epidemiological week of 2020. This incidence for Latin American countries was collected from the Pan American Health Organization (www.paho.org), which also provides dengue incidence data on a state level for Mexico. For Colombian states data was collected from bulletins made available by the Colombian Health Ministry (https://www.minsalud.gov.co). For other countries considered data was collected from disease threat reports provided by the European Centre for Disease Prevention and Control - (ECDC - www.ecdc.europa.eu).
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COVID-19 is the disease caused by the recent discovered Sars-CoV-2 virus in 2020. The virus was first detected in Wuhan, China and spread around the globe, causing a pandemic. This dataset contains data of the COVID-19 pandemic in the city of Limeira, SP, Brazil.
The city of Limeira is located at Sao Paulo State, in Brazil, and has almost 300k habitants. For deal with de pandemic, the city built the URC (Coronavirus Reference Unit), an area at one of the city hospitals for treatment to COVID cases only.
The data was acquired by scrapping the daily public bouletin from the official website of the city hall of Limeira.
This dataset was built along the year, to make data visualizations and some estimations, for better understanding the pandemic evolution in this city.
Furthermore, there is an expectation to build or apply machine learning models to predict confirmed cases and deaths.
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Analysis of ‘COVID-19 in Turkey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gkhan496/covid19-in-turkey on 28 January 2022.
--- Dataset description provided by original source is as follows ---
COVID-19 data in Turkey. Daily Covid-19 data published by our health ministry.
time_series_covid_19_confirmed_tr
time_series_covid_19_recovered_tr
time_series_covid_19_deaths_tr
time_series_covid_19_intubated_tr
time_series_covid_19_intensive_care_tr.csv
time_series_covid_19_tested_tr.csv
test_numbers : Number of test (daily)
Total data
covid_19_data_tr
Github repo : https://github.com/gkhan496/Covid19-in-Turkey/
We would like to thank our health ministry and all health workers.
USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases 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 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
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Source : https://fastlifehacks.com/n95-vs-ffp/
https://covid19.saglik.gov.tr https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR0k49fzqTxI4HBBZF7n4hLX4Zj0Q2KII_WOEo7agklC20KODB3TOeF8RrU#/bda7594740fd40299423467b48e9ecf6 http://who.int/ --situation reports https://evrimagaci.org/covid19#turkey-statistics
--- Original source retains full ownership of the source dataset ---
In April 2021, Brazil reached a new record of deaths due to COVID-19 in a day, with more than 4,200 thousand fatalities reported within 24 hours. That same month, the country's gross domestic product (GDP) was expected to increase by 3.17 percent during the year, down from a growth of nearly 3.5 percent forecast two months earlier. Since then, expectations have improved, with a forecast growth of 5.27 percent as of the third week of July.By December 2020, Brazil's GDP was forecast to decrease by 4.4 percent during 2020, an improvement in comparison to the 6.5 percent decrease forecast by the beginning of July. This figure, which had remained stable at a 2.3 percent forecast growth during the first months of the year, decreased for five consecutive months amidst the outbreak of COVID-19 in Brazil. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.
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BackgroundThe COVID-19 is a significant public health issue, and monitoring confirmed cases and deaths is an essential epidemiologic tool. We evaluated the features in Brazilian hospitalized patients due to severe acute respiratory infection (SARI) during the COVID-19 pandemic in Brazil. We grouped the patients into the following categories: Influenza virus infection (G1), other respiratory viruses' infection (G2), other known etiologic agents (G3), SARS-CoV-2 infection (patients with COVID-19, G4), and undefined etiological agent (G5).MethodsWe performed an epidemiological study using data from DataSUS (https://opendatasus.saude.gov.br/) from December 2019 to October 2021. The dataset included Brazilian hospitalized patients due to SARI. We considered the clinical evolution of the patients with SARI during the COVID-19 pandemic according to the SARI patient groups as the outcome. We performed the multivariate statistical analysis using logistic regression, and we adopted an Alpha error of 0.05.ResultsA total of 2,740,272 patients were hospitalized due to SARI in Brazil, being the São Paulo state responsible for most of the cases [802,367 (29.3%)]. Most of the patients were male (1,495,416; 54.6%), aged between 25 and 60 years (1,269,398; 46.3%), and were White (1,105,123; 49.8%). A total of 1,577,279 (68.3%) patients recovered from SARI, whereas 701,607 (30.4%) died due to SARI, and 30,551 (1.3%) did not have their deaths related to SARI. A major part of the patients was grouped in G4 (1,817,098; 66.3%) and G5 (896,207; 32.7%). The other groups account for
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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Abstract The Covid-19 pandemic challenges research institutions with the urgent need of responding to the morbidity and mortality caused by the virus. This study aimed to overview studies with humans on this disease in the first three months of 2020, in Brazil. Official data of the population and research protocols on Covid-19, distributed by Brazilian states, supported this temporal analysis. The incidence of the virus has grown exponentially, especially in the North and Northeast regions. Despite the discrete, slow, and asymmetric diffusion of studies, they are concentrated in the Southeast, and few clinical trials have entered Phase II. The geographical distribution of research ethics committees, higher education institutions, investments in science and technology, health centers and hospitals generate state vulnerabilities when addressing the disease. Close longitudinal follow-up should be carried out in the face of regional inequities, to defend bioethical principles and human life.
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Analysis of ‘Indonesia-Coronavirus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases on 30 September 2021.
--- Dataset description provided by original source is as follows ---
COVID-19 has infected many people in Indonesia, and the number of confirmed cases is increasing exponentially. Indonesia has raised its coronavirus alert to the "Darurat Nasional (National Emergency)" until 29 May 2020. The Java island, especially Jakarta, the capital city of Indonesia, is the most affected region by the coronavirus.
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Following are the list of available online portals announce the information of COVID-19, from the public community and provincial (regional) government website in Indonesia.
We make a structured dataset based on the report materials in these portals. Thus, the research community can apply recent AI and statistical techniques to generate new insights in support of the ongoing fight against this infectious disease in Indonesia.
Dataset 1) Total Confirmed Positive Cases 2) Google Trend Related keywords 3) Patient Epidemiological Data 4) Daily Case Statistics 5) Case per Province 6) Case in Jakarta Capital City 7) Daily New Confirmed Cases in Each Province (Timeline)
Kernel 1) Predicting Coronavirus Positive Cases in Indonesia 2) Visualization & Analysis of Covid-19 in Indonesia 3) Logistic Model for Indonesia COVID-19 4) DataSet Characteristics of Corona patients in several countries, including Indonesia 5) Novel Corona Virus (Covid-19) Indonesia EDA 6) Simple Visualization and Forecasting 7) Characteristics of Corona patients DS
Related Publication 1) Response to Covid-19: Data Analytics and Transparency, Koderea Talks, 18 March 2020, https://www.researchgate.net/publication/340003505_Response_to_Covid-19_Data_Analytics_and_Transparency 2) Covid-19 Data Science, ID Institute Obrolin Data Coronavirus, 24 March 2020, https://www.researchgate.net/publication/340116231_IDInstitute_Covid-19_Data_Science
Thanks sincerely to all the members of the DSCI Team, KawalCovid19.id, Pemda DKI Jakarta, Pemprov Jawa Barat, Pemprov Jawa Tengah, Pemprov Sumatera Barat, and Pemprov DIY.
We welcome anyone to join us as collaborators! Join WAG Chat: https://s.id/fgPoP For more information please contact ardi@ejnu.net or WA +8210-4297-0504
Working with
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--- Original source retains full ownership of the source dataset ---
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Please, If you enjoyed this dataset, don't forget to upvote it.
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.
If you are interested in know about another country, please follow these Kaggle datasets:
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Abstract The article aims to discuss the consequences of social distancing measures on the availability of blood and organization of blood therapy services at the beginning of the Covid-19 pandemic in Brazil. News published in April 2020 on the websites of the country’s state Blood Service Networks were consulted and organized in an Excel spreadsheet, presented in summary charts, and descriptions of results were prepared. A critical situation of blood supply, especially of some blood types, has been observed in many states. This situation is influenced by the circulation of the new coronavirus. The adoption of social distancing measures associated with unchanged transfusion demands for outpatient, urgency and emergency care required the implementation of strategies and actions for the reorganization of the services. Protection measures were incorporated, flows were changed and new routines were established. This study shows the extent to which the epidemiological situation of Covid-19 and the necessary measures for its control influenced the stocks and availability of blood. Changes in the organization of blood therapy services were fundamental in order to ensure protection, mitigate the risks of spreading the virus, and ensure the blood supply to meet the needs of the health system.
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IntroductionThe possibility that asthma is not a risk factor for the worst outcomes due to coronavirus disease (COVID-19) is encouraged. The increase in Th2 response dominance can downregulate the late phase of hyperinflammation, which is typically the hallmark of more severe respiratory viral infections, alongside lower angiotensin-converting enzyme receptors in patients with asthma due to chronic inflammation. Few studies associated asthma diagnosis and COVID-19 outcomes. In this context, we aimed to associate the asthma phenotype with the clinical signs, disease progression, and outcomes in patients with COVID-19.MethodsWe performed an epidemiologic study using patients’ characteristics from OpenDataSUS to verify the severity of COVID-19 among Brazilian hospitalized patients with and without the asthma phenotype according to the need for intensive care units, intubation, and deaths. We also evaluated the demographic data (sex, age, place of residence, educational level, and race), the profile of clinical signs, and the comorbidities.ResultsAsthma was present in 43,245/1,129,838 (3.8%) patients. Among the patients with asthma, 74.7% who required invasive ventilatory support evolved to death. In contrast, 78.0% of non-asthmatic patients who required invasive ventilatory support died (OR = 0.83; 95% CI = 0.79–0.88). Also, 20.0% of the patients with asthma that required non-invasive ventilatory support evolved to death, while 23.5% of non-asthmatic patients evolved to death (OR = 0.81; 95% CI = 0.79–0.84). Finally, only 11.2% of the patients with asthma who did not require any ventilatory support evolved to death, while 15.8% of non-asthmatic patients evolved to death (OR = 0.67; 95% CI = 0.62–0.72). In our multivariate analysis, one comorbidity and one clinical characteristic stood out as protective factors against death during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients with asthma were less prone to die than other patients (OR = 0.79; 95% CI = 0.73–0.85), just like puerperal patients (OR = 0.74; 95% CI = 0.56–0.97) compared to other patients.ConclusionAsthma was a protective factor for death in hospitalized patients with COVID-19 in Brazil. Despite the study’s limitations on patients’ asthma phenotype information and corticosteroid usage, this study brings to light information regarding a prevalent condition that was considered a risk factor for death in COVID-19, being ultimately protective.
Brazil is the country with the largest number of coronavirus (COVID-19) cases in Latin America. As of February 26, 2020 only one infection had been reported in Brazil. By August 19, 2021, the figure had exceeded 20 million. São Paulo is the state with the largest number of patients in the South American country.
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Supplementary Material of the article "Adverse events following immunization of elderly with COVID-19 inactivated virus vaccine (CoronaVac) in Southeastern Brazil: an active surveillance study"
The World Health Organization (WHO) characterized the COVID-19, caused by the SARS-CoV-2, as a pandemic on March 11, while the exponential increase in the number of cases was risking to overwhelm health systems around the world with a demand for ICU beds far above the existing capacity, with regions of Italy being prominent examples.
Brazil recorded the first case of SARS-CoV-2 on February 26, and the virus transmission evolved from imported cases only, to local and finally community transmission very rapidly, with the federal government declaring nationwide community transmission on March 20.
Until March 27, the state of São Paulo had recorded 1,223 confirmed cases of COVID-19, with 68 related deaths, while the county of São Paulo, with a population of approximately 12 million people and where Hospital Israelita Albert Einstein is located, had 477 confirmed cases and 30 associated death, as of March 23. Both the state and the county of São Paulo decided to establish quarantine and social distancing measures, that will be enforced at least until early April, in an effort to slow the virus spread.
One of the motivations for this challenge is the fact that in the context of an overwhelmed health system with the possible limitation to perform tests for the detection of SARS-CoV-2, testing every case would be impractical and tests results could be delayed even if only a target subpopulation would be tested.
This dataset contains anonymized data from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 RT-PCR and additional laboratory tests during a visit to the hospital.
All data were anonymized following the best international practices and recommendations. All clinical data were standardized to have a mean of zero and a unit standard deviation.
TASK 1 • Predict confirmed COVID-19 cases among suspected cases. Based on the results of laboratory tests commonly collected for a suspected COVID-19 case during a visit to the emergency room, would it be possible to predict the test result for SARS-Cov-2 (positive/negative)?
TASK 2 • Predict admission to general ward, semi-intensive unit or intensive care unit among confirmed COVID-19 cases. Based on the results of laboratory tests commonly collected among confirmed COVID-19 cases during a visit to the emergency room, would it be possible to predict which patients will need to be admitted to a general ward, semi-intensive unit or intensive care unit?
Submit a notebook that implements the full lifecycle of data preparation, model creation and evaluation. Feel free to use this dataset plus any other data you have available. Since this is not a formal competition, you're not submitting a single submission file, but rather your whole approach to building a model.
This is not a formal competition, so we won't measure the results strictly against a given validation set using a strict metric. Rather, what we'd like to see is a well-defined process to build a model that can deliver decent results (evaluated by yourself).
Our team will be looking at: 1. Model Performance - How well does the model perform on the real data? Can it be generalized over time? Can it be applied to other scenarios? Was it overfit? 2. Data Preparation - How well was the data analysed prior to feeding it into the model? Are there any useful visualisations? Does the reader learn any new techniques through this submission? A great entry will be informative, thought provoking, and fresh all at the same time. 3. Documentation - Are your code, and notebook, and additional data sources well documented so a reader can understand what you did? Are your sources clearly cited? A high quality analysis should be concise and clear at each step so the rationale is easy to follow and the process is reproducible.
Additional questions and clarifications can be obtained at data4u@einstein.br
Decision making by health care professionals is a complex process, when physicians see a patient for the first time with an acute complaint (e.g., recent onset of fever and respiratory symptoms) they will take a medical history, perform a physical examination, and will base their decisions on this information. To order or not laboratory tests, and which ones to order, is among these decisions, and there is no standard set of tests that are ordered to every individual or to a specific condition. This will depend on the complaints, the findings on the physical examination, personal medical history (e.g., current and prior diagnosed diseases, medications under use, prior surgeries, vaccination), lifestyle habits (e.g., smoking, alcohol use, exercising), family medical history, and prior exposures (e.g., traveling, occupation). The dataset reflects the complexity of decision making during routine clinical care, as opposed to what happens on a more controlled research setting, and data sparsity is, therefore, expected.
We understand that clinical and exposure data, in addition to the laboratory results, are invaluable information to be added to the models, but at this moment they are not available.
A main objective of this challenge is to develop a generalizable model that could be useful during routine clinical care, and although which laboratory exams are ordered can vary for different individuals, even with the same condition, we aimed at including laboratory tests more commonly order during a visit to the emergency room. So, if you found some additional laboratory test that was not included, it is because it was not considered as commonly order in this situation.
Hospital Israelita Albert Einstein would like to thank you for all the effort and time dedicated to this challenge, the community interest and the number of contributions have surpassed our expectations, and we are extremely satisfied with the results.
These have been challenging times, and we believe that promoting information sharing and collaboration will be crucial to gain insights, as fast as possible, that could help to implement measures to diminish the burden of COVID-19.
The multitude of solutions presented focusing on different aspects of the problem could represent a valuable resource in the evaluation of different strategies to implement predictive models for COVID-19. Besides the data visualization methods employed could make it easier for multidisciplinary teams to collaborate around COVID-19 real-world data.
Although this was not a competition, we would like to highlight some solutions, based on the community and our review of results.
Lucas Moda (https://www.kaggle.com/lukmoda/covid-19-optimizing-recall-with-smote) utilized interesting data visualization methods for the interpretability of models. Fellipe Gomes (https://www.kaggle.com/gomes555/task2-covid-19-admission-ac-94-sens-0-92-auc-0-96) used concise descriptions of the data and model results. We saw interesting ideas for visualizing and understanding the data, like the dendrogram used by CaesarLupum (https://www.kaggle.com/caesarlupum/brazil-against-the-advance-of-covid-19). Ossamu (https://www.kaggle.com/ossamum/eda-and-feat-import-recall-0-95-roc-auc-0-61) also sought to evaluate several data resampling techniques, to verify how it can improve the performance of predictive models, which was also done by Kaike Reis (https://www.kaggle.com/kaikewreis/a-second-end-to-end-solution-for-covid-19) . Jairo Freitas & Christian Espinoza (https://www.kaggle.com/jairofreitas/covid-19-influence-of-exams-in-recall-precision) sought to understand the distribution of exams regarding the outcomes of task 2, to support the decisions to be made in the construction of predictive models.
We thank you all for the feedback on available data, helping to show its potential, and taking the challenge of dealing with real data feed. Your efforts let the feeling that it is possible to build good predictive models in real life healthcare settings.
In 2019, energy consumption in the transport sector in Brazil amounted to ** million tons of oil equivalent (Mtoe). At the beginning of 2020, before the COVID-19 outbreak, this figure was forecast to increase to over ** Mtoe by the end of the year. However, in May 2020, after the lockdown measures ensued to control the spread of the virus, energy consumption in this sector was projected to decrease by more than ** percent in comparison to 2019. The commercial sector was expected to experience the largest drop, both in comparison to 2019 and to a pre-COVID-19 scenario. Meanwhile, consumption in the residential sector was expected to increase in the post-COVID-19 scenario.
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.