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.

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.

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.

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 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.

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

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.

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

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.

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.

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.

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.

The number of coronavirus disease (COVID-19)-positive cases has increased in Jeju Island, Republic of Korea. Identification and monitoring of new mutations in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are extremely important to fighting the global pandemic. We report a breakout of the B.1.620 lineage, harboring the E484 mutation in the virus spike protein in a general hospital on Jeju Island. A cluster of cases was detected between August 4 and September 10, 2021, involving 20 patients positive for COVID-19 of 286 individuals exposed to the virus, comprising hospital patients, staff, and caregivers. We analyzed the epidemiological characteristics and spike proteins mutation sites using Sanger sequencing and phylogenetic analysis on these 20 patients. By analyzing genomic variance, it was confirmed that 12 of the confirmed patients harbored the SARS-CoV-2 B.1.620 lineage. The breakthrough rate of infection was 2% in fully vaccinated individuals among these patients. Next clade analysis revealed that these SARS-CoV-2 genomes belong to clade 20A. This is the first reported case of SARS-CoV-2 sub-lineage B.1.620, although the B.1.617.2 lineage has prevailed in August and September in Jeju, which has a geographical advantage of being an island. We reaffirm that monitoring the spread of SARS-CoV-2 variants with characteristic features is indispensable for controlling COVID-19 outbreaks.

Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

As of August 28, 2023, around 50.6 percent of patients who died from novel coronavirus (COVID-19) in South Korea were female. During the same time period, women also made up the larger share of COVID-19 diagnoses in South Korea.

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

Context

This dataset is a curated version of 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE.

Content

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Data processing

From the original source of the data, we perform the following operations: * Concatenate the daily reports files (https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports)
* Add daily update date (as Date field)

• Fix country names duplicates. Multiple countries have duplicate names, ex: South Korea, Republic of Korea, Korea, South. data_df.loc[data_df['Country/Region']==' Azerbaijan', 'Country/Region'] = 'Azerbaijan' data_df.loc[data_df['Country/Region']=='Czechia', 'Country/Region'] = 'Czech Republic' data_df.loc[data_df['Country/Region']=="Cote d'Ivoire", 'Country/Region'] = 'Ivory Coast' data_df.loc[data_df['Country/Region']=='Iran (Islamic Republic of)', 'Country/Region'] = 'Iran' data_df.loc[data_df['Country/Region']=='Hong Kong SAR', 'Country/Region'] = 'Hong Kong' data_df.loc[data_df['Country/Region']=='Holy See', 'Country/Region'] = 'Vatican City' data_df.loc[data_df['Country/Region']=='Macao SAR', 'Country/Region'] = 'Macau' data_df.loc[data_df['Country/Region']=='Mainland China', 'Country/Region'] = 'China' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Korea, South', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Republic of Korea', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Moldova', 'Country/Region'] = 'Moldova' data_df.loc[data_df['Country/Region']=='Republic of the Congo', 'Country/Region'] = 'Congo (Brazzaville)' data_df.loc[data_df['Country/Region']=='Taiwan*', 'Country/Region'] = 'Taiwan' data_df.loc[data_df['Country/Region']=='The Gambia', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='Gambia, The', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='UK', 'Country/Region'] = 'United Kingdom' data_df.loc[data_df['Country/Region']=='Viet Nam', 'Country/Region'] = 'Vietnam'
• Replace missing data in Lat/Long for Province/State and/or Country/Region

data_df = pd.DataFrame()
for file in tqdm(os.listdir(db_source)):
  try:
    crt_date, crt_ext = crt_file = file.split(".")
    if(crt_ext == "csv"):
      crt_date_df = pd.read_csv(os.path.join(db_source, file))
      crt_date_df['date_str'] = crt_date
      crt_date_df['date'] = crt_date_df['date_str'].apply(lambda x: datetime.strptime(x, "%m-%d-%Y"))
      data_df = data_df.append(crt_date_df)
  except:
    pass

province_state = data_df['Province/State'].unique()

for ps in province_state:

  data_df.loc[(data_df['Province/State']==ps) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Province/State']==ps), 'Latitude'].median()
  
  data_df.loc[(data_df['Province/State']==ps) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Province/State']==ps), 'Longitude'].median() 

country_region = data_df['Country/Region'].unique()

for cr in country_region:

  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Country/Region']==cr), 'Latitude'].median()
  
  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Country/Region']==cr), 'Longitude'].median() 

Acknowledgements

Data source: https://github.com/CSSEGISandData/COVID-19

Inspiration

Represent the geographical data distribution of 2019-nCoV spread. Represent time series with Confirmed, Recovered, Deaths cases. Analyse the mortality. Try to forecast the evolution of cases. Compare the spread of Coronavirus for different countries, with different policies for social isolation, closing schools, stopping international travels.

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.

Available at https://youtu.be/5_2TR3l-Fhw. (TXT)

From the 4th to the 10th of May 2020, there were approximately 3.3 million cases of population movement recorded in South Korea. According to the source, this was around ten percent lower than the movement cases that were recorded before the COVID-19 outbreak in South Korea. The largest drop in movement happened during the fourth week after the outbreak (February 24th to March 1st), when population movement decreased by over 30 percent in comparison to before the COVID-19 outbreak in South Korea. All recorded cases post-outbreak were lower than the population movement figures recorded in 2019.

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

COVID-19 has intensified public apprehension about foreigners. In this article, we examine two questions related to public opinion on immigration. First, we assess the importance of cultural and economic factors in studying why individuals support or oppose immigration. Second, we examine the role of public health concerns in shaping attitudes towards open borders by priming the vaccination status of immigrants and the number of COVID-19 cases in their home countries. Using a conjoint analysis based on the data provided by nearly 1,700 respondents in South Korea, we find empirical support for both the existing explanations and public health concerns.