South Korea recorded 34610 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, South Korea reported 31415280 Coronavirus Cases. This dataset includes a chart with historical data for South Korea Coronavirus Deaths.

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....

Context

• A new coronavirus designated 2019-nCoV was first identified in Wuhan, the capital of China's Hubei province
• People developed pneumonia without a clear cause and for which existing vaccines or treatments were not effective
• The virus has shown evidence of human-to-human transmission
• Korea has defended well against coronavirus until summer, but it increased many confirmed cases from fall
• As of 24th Dec. approximately 53K cases have been confirmed, and daily around 1K cases are getting confirmed
• This datasets are prepared to cheer Korea up fighting against coronavirus

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4837224%2Ff829b8bd45aacf4c63b17e0116cb52c9%2Fcover_photo.PNG?generation=1608792447857317&alt=media" alt="">

Content

• 3 files attached which are 1) COVID Korea Status 2) COVID Korea Demo 3) COVID Korea Geo

• 1) COVID Korea Status : General daily update . STATE_DT : standard date . STATE_TIME : standard time . DECIDE_CNT : confirmed cases . CLEAR_CNT : clear cases after hospitalization . EXAM_CNT : examination cases . DEATH_CNT : death counts . CARE_CNT : counts on care . RESUTL_NEG_CNT : negative results after examination . ACC_EXAM_CNT : accumulative examination counts . ACC_EXAM_COMP_CNT: accumulative examination completes count . ACC_DEF_RATE : accumulative confirmed rate . CREATE_DT : posted date and time . UPDATE_DT : updated date and time

• 2) COVID Korea Demo : Updates with demographic information . GUBUN : classified by gender and age . CONF_CASE : confirmed cases . CONF_CASE_RATE : confirmed case rate . DEATH : death counts . DEATH_RATE : death rate . CRITICAL_RATE : critical rate . CREATE_DT : created date and time . UPDATE_DT : updated date and time

• 3) COVID Korea Geo : Updates with geographic information
  . CREATE_DT : created date and time
  . DEATH_CNT : death counts
  . GUBUN : city name
  . GUBUN_CN : city name in Chinese
  . GUBUN_EN : city name in English
  . INC_DEC : increase/decrease vs. past day
  . ISOL_CLEAR_CNT : clear counts from isolation
  . QUR_RATE : confirmed rate per 100K people
  . STD_DAY : standard day
  . UPDATE_DT : updated date and time
  . DEF_CNT : confirmed cases
  . ISOL_ING_CNT : isolated cases
  . OVER_FLOW_CNT : confirmed cases from foreign countries
  . LOCAL_OCC_CNT : domestic confirmed cases

Acknowledgements

• http://www.kdca.go.kr/cdc/
• https://data.go.kr/index.do

If these are useful, I will frequently update. Thanks.

South Korea recorded 147693 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, South Korea reported 4456 Coronavirus Deaths. This dataset includes a chart with historical data for South Korea Coronavirus Recovered.

This Project Tycho dataset includes a CSV file with COVID-19 data reported in KOREA (THE REPUBLIC OF): 2019-12-30 - 2021-07-31. It contains counts of cases, deaths, and demographics. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "COVID-19 Data Repository by Sang Woo Park", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

Context

Try to scrap data from official website of South Korea & France linked to COVID-19 confirmed cases and death in 2020

Content

Script to scrap data (France Publique Santé et South Korean KCDC) Results of scrapy : Data of COVID-19 confirmed cases & deaths Use direct link to differents sources : look at Acknowledgements

I use a very simple R0 model to try to evaluate what would happened without lock-down in Hubei, France, South-Korea, Italy in this https://www.kaggle.com/jeugregg/coronavirus-visualization-modeling

Acknowledgements

The world data is taken from https://github.com/CSSEGISandData/COVID-19 provided by JHU CSSE

South Korea areas data are retrieved with scrapy from online KCDC Press Release articles at https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030.

France areas data are taken with scrapy from online santepubliquefrance.fr Press articles at https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde and https://www.worldometers.info/coronavirus/country/france/ but until 25th March 2020.

For Global France, data are from https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/

For Global Italy, Germany, Hubei data are from https://www.worldometers.info/coronavirus/

Inspiration

What is the result of how each countries try to struggle this virus ?

Daily updates of Covid-19 Global Excess Deaths from the Economist's GitHub repository: https://github.com/TheEconomist/covid-19-the-economist-global-excess-deaths-model

Interpreting estimates

Estimating excess deaths for every country every day since the pandemic began is a complex and difficult task. Rather than being overly confident in a single number, limited data means that we can often only give a very very wide range of plausible values. Focusing on central estimates in such cases would be misleading: unless ranges are very narrow, the 95% range should be reported when possible. The ranges assume that the conditions for bootstrap confidence intervals are met. Please see our tracker page and methodology for more information.

New variants

The Omicron variant, first detected in southern Africa in November 2021, appears to have characteristics that are different to earlier versions of sars-cov-2. Where this variant is now dominant, this change makes estimates uncertain beyond the ranges indicated. Other new variants may do the same. As more data is incorporated from places where new variants are dominant, predictions improve.

Non-reporting countries

Turkmenistan and the Democratic People's Republic of Korea have not reported any covid-19 figures since the start of the pandemic. They also have not published all-cause mortality data. Exports of estimates for the Democratic People's Republic of Korea have been temporarily disabled as it now issues contradictory data: reporting a significant outbreak through its state media, but zero confirmed covid-19 cases/deaths to the WHO.

Acknowledgements

A special thanks to all our sources and to those who have made the data to create these estimates available. We list all our sources in our methodology. Within script 1, the source for each variable is also given as the data is loaded, with the exception of our sources for excess deaths data, which we detail in on our free-to-read excess deaths tracker as well as on GitHub. The gradient booster implementation used to fit the models is aGTBoost, detailed here.

Calculating excess deaths for the entire world over multiple years is both complex and imprecise. We welcome any suggestions on how to improve the model, be it data, algorithm, or logic. If you have one, please open an issue.

The Economist would also like to acknowledge the many people who have helped us refine the model so far, be it through discussions, facilitating data access, or offering coding assistance. A special thanks to Ariel Karlinsky, Philip Schellekens, Oliver Watson, Lukas Appelhans, Berent Å. S. Lunde, Gideon Wakefield, Johannes Hunger, Carol D'Souza, Yun Wei, Mehran Hosseini, Samantha Dolan, Mollie Van Gordon, Rahul Arora, Austin Teda Atmaja, Dirk Eddelbuettel and Tom Wenseleers.

All coding and data collection to construct these models (and make them update dynamically) was done by Sondre Ulvund Solstad. Should you have any questions about them after reading the methodology, please open an issue or contact him at sondresolstad@economist.com.

Suggested citation The Economist and Solstad, S. (corresponding author), 2021. The pandemic’s true death toll. [online] The Economist. Available at: https://www.economist.com/graphic-detail/coronavirus-excess-deaths-estimates [Accessed ---]. First published in the article "Counting the dead", The Economist, issue 20, 2021.

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

Effect of the coverage rate on COVID-19 death.

Data-set of the paper Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks for replication purpose of the Data Editor of AEJMacro. Detailed information on the data-set is in the readme file in the public repository openicpsr-175241 (under review).

We study the dynamic interaction between Covid-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for 1-2 months. These shocks are the main drivers of the pandemic, explaining between 20-60% of the average and historical variability in mobility, cases, and deaths worldwide. The policy tradeoff associated to non-pharmaceutical interventions is 1pp less economic mobility per day for 8% fewer deaths after three months.

The panel data-set contains the main data to perform the analysis in the paper. It contains dailiy data for (in sheets) Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Saudi Arabia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom and United States. Included variables are: Confirmed Cases, Total Deaths, Days Last Reported Case, Total Tests, School Closing, Workplace Closing, Cancel Public Events, Restrictions Gatherings, Close Public Transport, Stay at Home Requirements, Restrictions Internal Movement, International Travel Controls, Income Support, Debt/Contract Relief, Fiscal Measures, International Support, Public Information Campaigns, Testing Policy, Contact Tracing, Healthcare Emergency Investment, Investment Vaccines, Stringency Index, Small Cap, Large Cap, Government Benchmarks 3 Month, Government Benchmarks 1 Year, Government Benchmarks 2 Year, Government Benchmarks 5 Year, Government Benchmarks 10 Year, FX Indices Broad, FX Indices Narrow, Mobility Retail Mobility Grocery, Mobility Parks, Mobility Transit Stations Mobility Workplaces, Mobility Residential. Period: 30.12.2016 to 31.08.2020. All data are downloaded from Macrobond.

Context

Roughly a year has passed since the outbreak of COVID-19, in which South Korea has managed to contain the virus with relative success. However, the pandemic nevertheless caused great harm to the Korean public and the economy. Notably, recent weeks (December 2020) were marked by soaring numbers of infections in South Korea, where infections exceeded 1000 for the first time. Thus, analyzing the covid status data of South Korea may give insight into the trend of the pandemic.

Content

The time period of the data is January 1, 2020 - December 23, 2020. Updates may be provided later.

The covid_kr file contains information such as number of: confirmed cases, released from quarantine, tests, deaths, patients being treated, negative cases, aggregate tests etc.

The gender_age file contains information of cases based on age and gender category.

Please refer to the column descriptions for details :)

Acknowledgements

The data was provided by the Ministry of Health and Welfare, which I obtained through the open API in the Korean Public Data Portal.

Image Credit: PIRO4D from Pixabay

Note

Analysis such as the trend of infections, vulnerable age groups, and many others can be conducted. Other useful datasets may be uploaded later on if found.

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

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 information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Hehe

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

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 information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

BackgroundPatients undergoing hemodialysis (HD) have a high risk of novel coronavirus disease 2019 (COVID-19) and poor clinical outcomes. This study aimed to investigate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine effectiveness against infection and deaths in the South Korean population undergoing HD.MethodsWe conducted a retrospective cohort study to compare the incidence of COVID-19 and post-diagnosis mortality between patients who were either never vaccinated or fully or partially vaccinated. The Korean nationwide COVID-19 registry and the Korean National Health Insurance Service databases were used. Adult patients without a history of COVID-19 were included between October 8, 2020, and December 31, 2021. The study outcomes were COVID-19 diagnosis, severe clinical COVID-19-related events, and post-diagnosis death.ResultsEighty-five thousand eighteen patients undergoing HD were included, of whom 69,601 were fully vaccinated, 2,213 were partially vaccinated and 13,204 were unvaccinated. Compared with the unvaccinated group, the risk of being diagnosed with COVID-19 in patients who were fully vaccinated decreased during the study period (adjusted odds ratio [aOR] = 0.147; 95% confidence interval [CI] = 0.135–0.159). There were 1,140 (1.3%) patients diagnosed with COVID-19. After diagnosis, fully vaccinated patients were significantly less likely to die than unvaccinated patients (aOR = 0.940; 95% CI = 0.901–0.980) and to experience severe clinical events (aOR = 0.952; 95% CI = 0.916–0.988).ConclusionFull vaccination against COVID-19 was associated with a reduced risk of both infection and severe clinical outcomes in the South Korean population undergoing HD. These findings support the use of vaccination against SARS-CoV-2 among patients undergoing HD.

Context

The novel coronavirus that has infected more than 79,551 people worldwide (as of time of writing this context) is spreading rapidly, and independently, in countries outside of China, including Italy, South Korea, and Iran. The viral illness is being diagnosed among hundreds of people in South Korea, Italy and Iran who have no connection to China.

Content

In the notebook I use the time series data. Time series data columns are described in the column description.

Acknowledgements

Thanks to the Johns Hopkins University for providing this data-set for educational purposes. https://github.com/CSSEGISandData/COVID-19

Inspiration

To visualize COVID-19 spread world wide.

Abstract Mortality statistics due to COVID-19 worldwide are compared, by adjusting for the size of the population and the stage of the pandemic. Data from the European Centre for Disease Control and Prevention, and Our World in Data websites were used. Analyses are based on number of deaths per one million inhabitants. In order to account for the stage of the pandemic, the baseline date was defined as the day in which the 10th death was reported. The analyses included 78 countries and territories which reported 10 or more deaths by April 9. On day 10, India had 0.06 deaths per million, Belgium had 30.46 and San Marino 618.78. On day 20, India had 0.27 deaths per million, China had 0.71 and Spain 139.62. On day 30, four Asian countries had the lowest mortality figures, whereas eight European countries had the highest ones. In Italy and Spain, mortality on day 40 was greater than 250 per million, whereas in China and South Korea, mortality was below 4 per million. Mortality on day 10 was moderately correlated with life expectancy, but not with population density. Asian countries presented much lower mortality figures as compared to European ones. Life expectancy was found to be correlated with mortality.

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma. Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients (‘diagnosed’ and ‘hospitalized’) based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions. Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6–8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7–6.8) to 18.5% (95% CI 18.2–18.8) in the diagnosed cohort and 5.2% (95% CI 4.0–6.8) to 20.5% (95% CI 18.6–22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8–2.4) to 16.9% (95% CI 13.8–20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15–30% of hospitalized COVID-19 asthma patients. Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.KEY MESSAGESAsthma prevalence in COVID-19 patients varied internationally (5.2–20.5%).The prevalence of asthma exacerbation at presentation with COVID-19 in diagnosed and hospitalized patients was low.Comorbidities were common in COVID-19 patients with asthma. KEY MESSAGES Asthma prevalence in COVID-19 patients varied internationally (5.2–20.5%).The prevalence of asthma exacerbation at presentation with COVID-19 in diagnosed and hospitalized patients was low.Comorbidities were common in COVID-19 patients with asthma. Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Context

COVID-19 data in Turkey. Daily Covid-19 data published by our health ministry.

Content

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

Github repo : https://github.com/gkhan496/Covid19-in-Turkey/

Acknowledgements

We would like to thank our health ministry and all health workers.

Country level datasets

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2311214%2Feaf61a1cf97850b64aefd52d3de5890b%2FXMhaJ.png?generation=1586182028591623&alt=media" alt="">

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

Context

Our primary objective is to commit our data and ideas into code so that we can share these ideas with true Data Scientists to be used to better understand this pandemic. Our current model uses the most current data available to create a predictive these models by country/region to estimate the maximum of Confirmed Cases by country/region and create reasonable a timeline to go with it.

Content

Most of us are familiar with the data. China (mainly Hubei), was at the epicenter of this pandemic starting around January 22, 2020, and from there on to Europe and then around the world. Since the far east is more mature in this situation, we are already seeing certain areas flatten out in their cases of COVID; namely Hubei, China and South Korea. Other than that most countries are still in the growth stage of their development. However, from Hubei and South Korea we were able to fit regression curves to these data. Of noticeable importance was a version of the Sigmoid curve-fit equation as shown below. Yes, there are other equations that had better fits (r2); however, the Sigmoid equation has meaningful fit parameters that stand for something to us the users.

Acknowledgements

We have studied and openly used code from covid-19-digging-a-bit-deeper and COVID Global Forecast: SIR model + ML regressions as go-by's in the preparation of this notebook. These were both great notebooks that allowed this non-programmer to at least share some ideas in the spirit of collaboration.

Inspiration

These COVID data have certain characteristics by country/region as pointed out by Tomas Pueyo in the Medium article, "Coronavirus: The Hammer and the Dance". Tomas did an excellent job of describing these artifacts in the Hubei data in relationship to what he called the Hammer and the Dance and this gave us insight into interpreting the data from South Korea and hopefully the rest of the world soon .

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.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Inspiration

Some useful insi...