View daily updates and historical trends for South Korea Coronavirus Cases Per Day. Source: Johns Hopkins Center for Systems Science and Engineering. Trac…

On March 1, 2023, exactly 12,291 new cases of coronavirus (COVID-19) were reported in South Korea. South Korea's handling of the coronavirus (COVID-19) was initially widely praised, though the government's handling of vaccine distribution has been criticized. Seoul and the metropolitan areas were hit especially hard by a few group infections during the second wave in August 2020. This was followed by a fourth wave, driven by the delta variant and low vaccination rates, leading to rising figures. Though the country has since achieved high vaccination rates, the omicron variant led to record new daily cases. Cases once again began to decline in January of 2023.

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

As of July 3, 2023, South Korea has confirmed a total of 32,256,154 cases of coronavirus (COVID-19) within the country, including 35,071 deaths. South Korea's handling of the coronavirus (COVID-19) was initially widely praised, though the government's handling of vaccine distribution has been criticized. After the first wave lasted till April, Seoul and the metropolitan areas were hit hard by a few group infections during the second wave in August 2020. This was followed by a fourth wave, driven by the delta variant and low vaccination rates, leading to rising figures. Though the country has since achieved high vaccination rates, the omicron variant led to record new daily cases in 2022.

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

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

As of August 28, 2023, South Korea has confirmed a total of 34,436,586 positive cases of coronavirus (COVID-19), including 35,812 deaths. The first case coronavirus in South Korea was discovered in January 2020. Currently, 25.57 cases per 100,000 people are being confirmed, down from 35.74 cases last month.

Case development trend

In the middle of February 2020, novel coronavirus (COVID-19) began to increase exponentially from patient 31, who was known as a super propagator. With a quick response by the government, the daily new cases once dropped to a single-digit. In May 2020, around three hundreds of new infections were related to cluster infections that occurred in some clubs at Itaewon, an entertainment district in Seoul. Seoul and the metropolitan areas were hit hard by this Itaewon infection. Following the second wave of infections in August, the government announced it was facing the third wave in November with 200 to 300 confirmed cases every day. A fourth wave started in July 2021 from the spread of the delta variant and low vaccination rates. While vaccination rates have risen significantly since then, the highly infectious omicron variant led to a record-breaking rise in cases. This began easing up in March of 2022, though numbers began to rise again around August of 2022. As of October 2022, case numbers are decreasing again.

Economic impact on Korean economy

The Korean economy is interdependent on many countries over the world, so the impact of coronavirus on Korean economy is significant. According to recent OECD forecasts, South Korea's GDP is projected to show positive growth in 2022 and 2023. The first sector the coronavirus impacted was tourism, caused by decreasing numbers of inbound tourists and domestic sales. In the first quarter of 2020, tourism revenue was expected to decrease by 2.9 trillion won. In addition, Korean companies predicted that the damage caused by the losses in sales and exports would be significant. In particular, the South Korean automotive industry was considered to be the most affected industry, as automobile production and parts supply stopped at factories in China.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In past 24 hours, S. Korea, Asia had N/A new cases, N/A deaths and N/A recoveries.

The outbreak of the novel coronavirus in Wuhan, China, saw infection cases spread throughout the Asia-Pacific region. By April 13, 2024, India had faced over 45 million coronavirus cases. South Korea followed behind India as having had the second highest number of coronavirus cases in the Asia-Pacific region, with about 34.6 million cases. At the same time, Japan had almost 34 million cases. At the beginning of the outbreak, people in South Korea had been optimistic and predicted that the number of cases would start to stabilize. What is SARS CoV 2?Novel coronavirus, officially known as SARS CoV 2, is a disease which causes respiratory problems which can lead to difficulty breathing and pneumonia. The illness is similar to that of SARS which spread throughout China in 2003. After the outbreak of the coronavirus, various businesses and shops closed to prevent further spread of the disease. Impacts from flight cancellations and travel plans were felt across the Asia-Pacific region. Many people expressed feelings of anxiety as to how the virus would progress. Impact throughout Asia-PacificThe Coronavirus and its variants have affected the Asia-Pacific region in various ways. Out of all Asia-Pacific countries, India was highly affected by the pandemic and experienced more than 50 thousand deaths. However, the country also saw the highest number of recoveries within the APAC region, followed by South Korea and Japan.

View daily updates and historical trends for South Korea Coronavirus Cases. Source: Johns Hopkins Center for Systems Science and Engineering. Track econom…

As of August 31, 2023, Gyeonggi (Gyeonggi Province) registered around 9.3 million confirmed cases of coronavirus (COVID-19), making it the region with the most cases in South Korea. This was followed by the capital city of Seoul and the city of Busan. A further 18.9 thousand people tested positive during quarantine. There were a total of 14 provinces with one million or more COVID cases each, with Gangwon (Gangwon Province) being the newest addition.

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

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....

As of August 28, 2023, confirmed coronavirus (COVID-19) patients in their forties made up the largest share of patients in South Korea, amounting to around 15.2 percent of all positive cases. The first wave lasted until April, with the second wave following in August of 2020. This was further followed by a fourth wave, driven by the delta and omicron variants. Though the country has since achieved high vaccination rates, the omicron variant led to record new daily cases in 2022.

Patient profile

In South Korea, the infection rate of coronavirus was the highest among people in the twenties due to their social activities. Indeed, the new infections related to the clubgoers in Seoul are likely to increase the infection rate between young people. 158 out of 261 clubgoer-related confirmed patients were in teenagers or in their twenties, and 36 patients were in their thirties. The mortality rate of coronavirus by age group was somewhat different from the age distribution of total infection cases. It was highest among people in their eighties, with this group making up around 59.6 percent of deaths related to the coronavirus in South Korea. Mortality declined with each younger age group.

Daily life changes

In South Korea, a new policy of "With Corona" has been launched in order to ease society back into a new norm of living with the virus, without having too many restrictions in place. This is based on high vaccination rates, and includes strict quarantine measures for those who are infected and their close contacts. There are plans to improve the verification of vaccination and test certificates for use in public spaces. Most South Koreans have responded to rising numbers by once again avoiding crowded places or going out. It is common to wear masks regardless of diseases, so people are continuing to wear masks when they need to go out. Also, people prefer to do online shopping than physical shopping, and online sales of food and health-related products have increased by more than 700 percent compared to last year. Spending on living, cooking, and furniture has increased significantly as people spend more time at home.

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.

As of August 28, 2023, around 54 percent of confirmed coronavirus (COVID-19) patients in South Korea were female. South Korea's handling of the coronavirus (COVID-19) was initially widely praised, though the government's handling of vaccine distribution has been criticized. The first wave lasted until April, after which Seoul and the metropolitan areas were hit hard by a few group infections during the second wave in August 2020. This was followed by a fourth wave, driven by the delta variant and low vaccination rates, leading to rising figures. Though the country has since achieved high vaccination rates, the omicron variant led to record new daily cases in 2022.

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

New Covid cases per month in North Korea, March, 2023 The most recent value is 0 new Covid cases as of March 2023, compared to the previous value of 0 new Covid cases. Historically, the average for North Korea from February 2020 to March 2023 is new Covid cases. The minimum of new Covid cases was recorded in , while the maximum of new Covid cases was reached in . | TheGlobalEconomy.com

As of August 1, 2021, over 11.7 million coronavirus (COVID-19) tests were conducted in South Korea. South Korea succeeded in flattening the infection curve by rapidly conducting extensive tests immediately in the early stages and exported medical products and hygiene products to other countries. However, from July 2021, Korea has been dealing with a fourth wave because of the spread of the delta variant and low vaccination numbers. As of August 13, 2021, South Korea confirmed 220,182 cases of infection including 2,144 deaths.

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

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 ?

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.

The COVID-19 pandemic has necessitated the development of robust tools for tracking and modeling the spread of the virus. We present ‘K-Track-Covid,’ an interactive web-based dashboard developed using the R Shiny framework, to offer users an intuitive dashboard for analyzing the geographical and temporal spread of COVID-19 in South Korea. Our dashboard employs dynamic user interface elements, employs validated epidemiological models, and integrates regional data to offer tailored visual displays. The dashboard allows users to customize their data views by selecting specific time frames, geographic regions, and demographic groups. This customization enables the generation of charts and statistical summaries pertinent to both daily fluctuations and cumulative counts of COVID-19 cases, as well as mortality statistics. Additionally, the dashboard offers a simulation model based on mathematical models, enabling users to make predictions under various parameter settings. The dashboard is designed to assist researchers, policymakers, and the public in understanding the spread and impact of COVID-19, thereby facilitating informed decision-making. All data and resources related to this study are publicly available to ensure transparency and facilitate further research.

This site provides comprehensive statistical information on the status of COVID-19 infections with data (cumulative confirmation rate, cumulative number of tests, cumulative number of tests completed, number of patients receiving treatment, number released from quarantine, number of deaths, number of confirmed cases, number of negative results, date of application of standard, time of application, number of tests in progress).

As of August 28, 2023, around 59.8 percent of the patients who died from novel coronavirus (COVID-19) in South Korea were aged 80 years or older. This was despite older people making up only a small percentage of all COVID-19 cases in South Korea. A fourth wave fueled by the delta and omicron variants led to a record rate of new daily cases in 2022, which once again began to decline in 2023.

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