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.

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.

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.

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.

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

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.

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.

(South Korea) 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea

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.

This provides a function to search for information on the status of COVID-19 patients entering the country from overseas using data on the status of overseas inflow of COVID-19 patients. ※ Data source: Korea Disease Control and Prevention Agency (COVID-19 website) ※ The Korea Disease Control and Prevention Agency is strengthening quarantine at major entry points such as airports and ports, and is managing overseas inflow cases and providing information on them through diagnostic testing and self-quarantine measures for entrants. ※ We would like to inform you that updates to data on the status of overseas inflow of COVID-19 patients will be discontinued from June 1, 2023 in accordance with the decision of the Central Disaster and Safety Countermeasures Headquarters (Korea Disease Control and Prevention Agency) to lower the COVID-19 crisis level and switch to quarantine measures.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.-- Esri COVID-19 Trend Report for 3-9-2023 --0 Countries have Emergent trend with more than 10 days of cases: (name : # of active cases) 41 Countries have Spreading trend with over 21 days in new cases curve tail: (name : # of active cases)Monaco : 13, Andorra : 25, Marshall Islands : 52, Kyrgyzstan : 79, Cuba : 82, Saint Lucia : 127, Cote d'Ivoire : 148, Albania : 155, Bosnia and Herzegovina : 172, Iceland : 196, Mali : 198, Suriname : 246, Botswana : 247, Barbados : 274, Dominican Republic : 304, Malta : 306, Venezuela : 334, Micronesia : 346, Uzbekistan : 356, Afghanistan : 371, Jamaica : 390, Latvia : 402, Mozambique : 406, Kosovo : 412, Azerbaijan : 427, Tunisia : 528, Armenia : 594, Kuwait : 716, Thailand : 746, Norway : 768, Croatia : 847, Honduras : 1002, Zimbabwe : 1067, Saudi Arabia : 1098, Bulgaria : 1148, Zambia : 1166, Panama : 1300, Uruguay : 1483, Kazakhstan : 1671, Paraguay : 2080, Ecuador : 53320 Countries may have Spreading trend with under 21 days in new cases curve tail: (name : # of active cases)61 Countries have Epidemic trend with over 21 days in new cases curve tail: (name : # of active cases)Liechtenstein : 48, San Marino : 111, Mauritius : 742, Estonia : 761, Trinidad and Tobago : 1296, Montenegro : 1486, Luxembourg : 1540, Qatar : 1541, Philippines : 1915, Ireland : 1946, Brunei : 2010, United Arab Emirates : 2013, Denmark : 2111, Sweden : 2149, Finland : 2154, Hungary : 2169, Lebanon : 2208, Bolivia : 2838, Colombia : 3250, Switzerland : 3321, Peru : 3328, Slovakia : 3556, Malaysia : 3608, Indonesia : 3793, Portugal : 4049, Cyprus : 4279, Argentina : 5050, Iran : 5135, Lithuania : 5323, Guatemala : 5516, Slovenia : 5689, South Africa : 6604, Georgia : 7938, Moldova : 8082, Israel : 8746, Bahrain : 8932, Netherlands : 9710, Romania : 12375, Costa Rica : 12625, Singapore : 13816, Serbia : 14093, Czechia : 14897, Spain : 17399, Ukraine : 19568, Canada : 24913, New Zealand : 25136, Belgium : 30599, Poland : 38894, Chile : 41055, Australia : 50192, Mexico : 65453, United Kingdom : 65697, France : 68318, Italy : 70391, Austria : 90483, Brazil : 134279, Korea - South : 209145, Russia : 214935, Germany : 257248, Japan : 361884, US : 6440500 Countries may have Epidemic trend with under 21 days in new cases curve tail: (name : # of active cases) 54 Countries have Controlled trend: (name : # of active cases)Palau : 3, Saint Kitts and Nevis : 4, Guinea-Bissau : 7, Cabo Verde : 8, Mongolia : 8, Benin : 9, Maldives : 10, Comoros : 10, Gambia : 12, Bhutan : 14, Cambodia : 14, Syria : 14, Seychelles : 15, Senegal : 16, Libya : 16, Laos : 17, Sri Lanka : 19, Congo (Brazzaville) : 19, Tonga : 21, Liberia : 24, Chad : 25, Fiji : 26, Nepal : 27, Togo : 30, Nicaragua : 32, Madagascar : 37, Sudan : 38, Papua New Guinea : 38, Belize : 59, Egypt : 60, Algeria : 64, Burma : 65, Ghana : 72, Haiti : 74, Eswatini : 75, Guyana : 79, Rwanda : 83, Uganda : 88, Kenya : 92, Burundi : 94, Angola : 98, Congo (Kinshasa) : 125, Morocco : 125, Bangladesh : 127, Tanzania : 128, Nigeria : 135, Malawi : 148, Ethiopia : 248, Vietnam : 269, Namibia : 422, Cameroon : 462, Pakistan : 660, India : 4290 41 Countries have End Stage trend: (name : # of active cases)Sao Tome and Principe : 1, Saint Vincent and the Grenadines : 2, Somalia : 2, Timor-Leste : 2, Kiribati : 8, Mauritania : 12, Oman : 14, Equatorial Guinea : 20, Guinea : 28, Burkina Faso : 32, North Macedonia : 351, Nauru : 479, Samoa : 554, China : 2897, Taiwan* : 249634 -- SPIKING OF NEW CASE COUNTS --20 countries are currently experiencing spikes in new confirmed cases:Armenia, Barbados, Belgium, Brunei, Chile, Costa Rica, Georgia, India, Indonesia, Ireland, Israel, Kuwait, Luxembourg, Malaysia, Mauritius, Portugal, Sweden, Ukraine, United Kingdom, Uzbekistan 20 countries experienced a spike in new confirmed cases 3 to 5 days ago: Argentina, Bulgaria, Croatia, Czechia, Denmark, Estonia, France, Korea - South, Lithuania, Mozambique, New Zealand, Panama, Poland, Qatar, Romania, Slovakia, Slovenia, Switzerland, Trinidad and Tobago, United Arab Emirates 47 countries experienced a spike in new confirmed cases 5 to 14 days ago: Australia, Austria, Bahrain, Bolivia, Brazil, Canada, Colombia, Congo (Kinshasa), Cyprus, Dominican Republic, Ecuador, Finland, Germany, Guatemala, Honduras, Hungary, Iran, Italy, Jamaica, Japan, Kazakhstan, Lebanon, Malta, Mexico, Micronesia, Moldova, Montenegro, Netherlands, Nigeria, Pakistan, Paraguay, Peru, Philippines, Russia, Saint Lucia, Saudi Arabia, Serbia, Singapore, South Africa, Spain, Suriname, Thailand, Tunisia, US, Uruguay, Zambia, Zimbabwe 194 countries experienced a spike in new confirmed cases over 14 days ago: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Costa Rica, Cote d'Ivoire, Croatia, Cuba, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea - South, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan*, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, US, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Yemen, Zambia, Zimbabwe Strongest spike in past two days was in US at 64,861 new cases.Strongest spike in past five days was in US at 64,861 new cases.Strongest spike in outbreak was 424 days ago in US at 1,354,505 new cases. Global Total Confirmed COVID-19 Case Rate of 8620.91 per 100,000Global Active Confirmed COVID-19 Case Rate of 37.24 per 100,000Global COVID-19 Mortality Rate of 87.69 per 100,000 21 countries with over 200 per 100,000 active cases.5 countries with over 500 per 100,000 active cases.3 countries with over 1,000 per 100,000 active cases.1 country with over 2,000 per 100,000 active cases.Nauru is worst at 4,354.54 per 100,000.

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

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.

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/

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 ---

Context

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Ff46e130bad5d4e74a8835ca057dd05ca%2Facc.png?generation=1584939612835429&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2F93b53d1b6601da74041f41ea4ba227f6%2Fcases.png?generation=1584938551413887&alt=media" alt="">

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.

1. https://www.covid19.go.id/situasi-virus-corona/, by Indonesian National Board for Disaster Management.
2. https://kawalcovid19.id/, by Kawal Covid-19 Indonesia community.
3. https://corona.jakarta.go.id/, Jakarta tanggap Covid-19 by Pemda DKI Jakarta.
4. https://pikobar.jabarprov.go.id/, Pusat Informasi & Koordinasi COVID-19, by Pemprov Jawa Barat.
5. https://corona.jatengprov.go.id/, Jawa Tengah Tanggap COVID-19, by Pemprov Jawa Tengah.
6. https://corona.sumbarprov.go.id/, Sumbar Tanggap Corona, by Pemprov Sumatera Barat.
7. http://corona.jogjaprov.go.id/, Yogyakarta Tanggap Covid-19, by Pemprov DIY.
8. https://covid19.bandung.go.id/. Pusat Informasi & Koordinasi COVID-19 Kota Bandung.

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.

Current State

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

Other Country Level Datasets

• 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
• 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

Acknowledgements

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.

DSCI Team

1. Ardiansyah (ardisragen)
2. Tri A Sundara (trilabs)
3. Thomhert (thomhert)
4. Epsi Sayidina (epsisayidina)
5. Teuku Hashrul (hahasrul)
6. Naufal Hakim (hakimbazol)

Invitation

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 https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2849532%2Fd56eaf0a5d770d756a54cec0d09c87ff%2Fkoderea.png?generation=1584539195622597&alt=media" alt="">

--- Original source retains full ownership of the source dataset ---

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 ---

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

--- Original source retains full ownership of the source dataset ---

This Project Tycho dataset includes a CSV file with COVID-19 data reported in KOREA (DEMOCRATIC PEOPLE'S REPUBLIC OF): 2020-01-03 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

This service provides a query function for the status of COVID-19 outbreak by city/province, including the cumulative number of confirmed cases, daily increase or decrease in confirmed cases, cumulative number of releases from quarantine, number of quarantined patients, number of local outbreaks, and number of overseas inflows. * Based on 17 metropolitan areas (Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, Sejong, Gyeonggi, Gangwon, Chungbuk, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, Gyeongnam, Jeju) ※ Data source: Korea Disease Control and Prevention Agency (Coronavirus Disease-19 website) ※ We would like to inform you that as the Central Disaster and Safety Countermeasure Headquarters (Korea Disease Control and Prevention Agency) has upgraded the COVID-19 infectious disease status to Level 4, the update of the COVID-19 outbreak status data by city/province will be discontinued from September 1, 2023.

IntroductionQuantifying the transmissibility over time, particularly by region and age, using parameters such as serial interval and time-varying reproduction number, helps in formulating targeted interventions. Moreover, considering the impact of geographical factors on transmission provides valuable insights into the effectiveness of control measures.MethodsDrawing on a comprehensive dataset of COVID-19 cases in South Korea, we analyzed transmission dynamics with a focus on age and regional variations. The dataset, compiled through the efforts of dedicated epidemiologists, includes information on symptom onset dates, enabling detailed investigations. The pandemic was divided into distinct phases, aligning with changes in policies, emergence of variants, and vaccination efforts. We analyzed various interventions such as social distancing, vaccination rates, school closures, and population density. Key parameters like serial interval, heatmaps, and time-varying reproduction numbers were used to quantify age and region-specific transmission trends.ResultsAnalysis of transmission pairs within age groups highlighted the significant impact of school closure policies on the spread among individuals aged 0-19. This analysis also shed light on transmission dynamics within familial and educational settings. Changes in confirmed cases over time revealed a decrease in spread among individuals aged 65 and older, attributed to higher vaccination rates. Conversely, densely populated metropolitan areas experienced an increase in confirmed cases. Examination of time-varying reproduction numbers by region uncovered heterogeneity in transmission patterns, with regions implementing strict social distancing measures showing both increased confirmed cases and delayed spread, indicating the effectiveness of these policies.DiscussionOur findings underscore the importance of evaluating and tailoring epidemic control policies based on key COVID-19 parameters. The analysis of social distancing measures, school closures, and vaccine impact provides valuable insights into controlling transmission. By quantifying the impact of these interventions on different age groups and regions, we contribute to the ongoing efforts to combat the COVID-19 pandemic effectively.