The distribution of coronavirus disease (COVID-19) cases in Japan as of March 16, 2022, showed that the highest number of patients were aged 20 to 29 years old, with a total of over one million cases. The highest number of deaths could be seen among the patients aged 80 years and older at about 15.5 thousand cases.

 Shortage of intensive care beds 

With over 1,200 hospital beds per 100,000 inhabitants available in the country, Japan is one of the best-equipped OECD nations regarding the medical sector. However, after the COVID-19 outbreak, country has faced a shortage of hospital beds, especially those required for intensive care. ICU beds only constitute a small share of the overall number of hospital beds in the country compared to European countries like Switzerland and Germany. To combat this problem, the Japanese government implemented financial incentives for hospitals upon acquisition of new intensive care beds. Another factor playing a significant part in the shortage of hospital beds is the comparably high average length of hospital stays, since some bedridden seniors are in long-term care in hospitals, as opposed to being cared for in nursing homes or at home.

Challenges for private hospitals Japan’s over eight thousand hospitals were opened by doctors, leading to the majority of the institutions being privately owned. As many of them are specialized and dependent on outpatient surgeries, COVID-19 patients pose new difficulties, as treating them in a converted ward would hinder day-to-day operations. Acquisition of intensive care beds involves financial and logistical challenges, which smaller private institutions have difficulty meeting, as they are not funded by tax revenues.

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

Japan recorded 33803572 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Japan reported 31547 Coronavirus Deaths. This dataset includes a chart with historical data for Japan Coronavirus Cases.

As of March 17, 2022, the highest number of approximately 1.2 million patients with coronavirus (COVID-19) were confirmed in Tokyo Prefecture in Japan, followed by Osaka Prefecture with about 747.9 thousand people. On that day, all prefectures out of 47 reported new infection cases.

 Tokyo and Kanagawa  The first coronavirus case in Japan was confirmed on January 16, 2020, in Kanagawa prefecture. Part of the Greater Tokyo Area, Kanagawa is the country’s second-most populous prefecture with more than nine million inhabitants. A few days after the first case in Kanagawa, Japan’s second case was reported in Tokyo. Kanagawa and Tokyo, along with Osaka, and four other prefectures, were the first to be placed under a state of emergency by then prime minister Shinzo Abe in April 2020. From the outbreak of COVID-19 until March 2022, the state of emergency was announced four times for Tokyo and three times for Kanagawa Prefecture.

 Osaka Osaka prefecture reported its first case of COVID-19 on January 29, 2020. The prefecture is the center of Japan’s second-most populated urban region, the Keihanshin metropolitan area, which includes Kyoto and Hyogo prefectures. The virus continued to spread in Osaka with the acceleration of new infection cases per day recorded in January, April to May, July to September in 2021, and January and onwards in 2022.

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

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

Analysis of ‘COVID-19 dataset in Japan’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lisphilar/covid19-dataset-in-japan on 28 January 2022.

--- Dataset description provided by original source is as follows ---

1. Context

This is a COVID-19 dataset in Japan. This does not include the cases in Diamond Princess cruise ship (Yokohama city, Kanagawa prefecture) and Costa Atlantica cruise ship (Nagasaki city, Nagasaki prefecture). - Total number of cases in Japan - The number of vaccinated people (New/experimental) - The number of cases at prefecture level - Metadata of each prefecture

Note: Lisphilar (author) uploads the same files to https://github.com/lisphilar/covid19-sir/tree/master/data

This dataset can be retrieved with CovsirPhy (Python library).

pip install covsirphy --upgrade

import covsirphy as cs
data_loader = cs.DataLoader()
japan_data = data_loader.japan()
# The number of cases (Total/each province)
clean_df = japan_data.cleaned()
# Metadata
meta_df = japan_data.meta()

Please refer to CovsirPhy Documentation: Japan-specific dataset.

Note: Before analysing the data, please refer to Kaggle notebook: EDA of Japan dataset and COVID-19: Government/JHU data in Japan. The detailed explanation of the build process is discussed in Steps to build the dataset in Japan. If you find errors or have any questions, feel free to create a discussion topic.

1.1 Total number of cases in Japan

covid_jpn_total.csv Cumulative number of cases: - PCR-tested / PCR-tested and positive - with symptoms (to 08May2020) / without symptoms (to 08May2020) / unknown (to 08May2020) - discharged - fatal

The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with mild symptoms (to 08May2020) / severe symptoms / unknown (to 08May2020) - requiring hospitalization, but waiting in hotels or at home (to 08May2020)

In primary source, some variables were removed on 09May2020. Values are NA in this dataset from 09May2020.

Manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)

The number of vaccinated people: - Vaccinated_1st: the number of vaccinated persons for the first time on the date - Vaccinated_2nd: the number of vaccinated persons with the second dose on the date - Vaccinated_3rd: the number of vaccinated persons with the third dose on the date

Data sources for vaccination: - To 09Apr2021: 厚生労働省 HP 新型コロナワクチンの接種実績(in Japanese) - 首相官邸 新型コロナワクチンについて - From 10APr2021: Twitter: 首相官邸（新型コロナワクチン情報）

1.2 The number of cases at prefecture level

covid_jpn_prefecture.csv Cumulative number of cases: - PCR-tested / PCR-tested and positive - discharged - fatal

The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with severe symptoms (from 09May2020)

Using pdf-excel converter, manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)

Note: covid_jpn_prefecture.groupby("Date").sum() does not match covid_jpn_total. When you analyse total data in Japan, please use covid_jpn_total data.

1.3 Metadata of each prefecture

covid_jpn_metadata.csv - Population (Total, Male, Female): 厚生労働省 厚生統計要覧（2017年度）第１－５表 - Area (Total, Habitable): Wikipedia 都道府県の面積一覧 (2015)

• Hospital_bed: With the primary data of 厚生労働省 感染症指定医療機関の指定状況（平成31年4月1日現在）, 厚生労働省 第二種感染症指定医療機関の指定状況（平成31年4月1日現在）, 厚生労働省 医療施設動態調査（令和２年１月末概数）, 厚生労働省 感染症指定医療機関について and secondary data of COVID-19 Japan 都道府県別 感染症病床数,

  • Specific: Hospital beds of medical institutions designated for specific infectious diseases
  • Type-I: Hospital beds of medical institutions designated for type I infectious diseases
  • Type-II: Hospital beds of medical institutions designated for type II infectious diseases
  • Tuberculosis: Hospital beds of medical institutions designated for tuberculosis (outpatient care)
  • Care: long term care bed of hospitals
  • Total: Beds of all hospitals

• Clinic_bed: With the primary data of 医療施設動態調査（令和２年１月末概数） ,

  • Care: long term care beds of clinics
  • Total: Beds of all clinics

• Location: Data is from LinkData 都道府県庁所在地 (Public Domain) (secondary data).

  • Latitude
  • Longitude

• Admin

  • Capital: Prefectural capital city. Data is from LinkData 都道府県庁所在地 (Public Domain) (secondary data).
  • Region: Region name. Data is from WIkipedia (secondary data). "Kyushu-Okinawa region" was separated to "Kyushu" and "Okinawa" by this datasets' author.
  • Num: Prefecture code (JIS X 0401: Hokkaido=1,...Okinawa=47). Data is from 国土交通省 GIS HP Pref code. cf. (not source) Japan VIsitor: Japan Prefectures Map.

2. Acknowledgements

To create this dataset, edited and transformed data of the following sites was used.

厚生労働省 Ministry of Health, Labour and Welfare, Japan:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English) 厚生労働省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)

国土交通省 Ministry of Land, Infrastructure, Transport and Tourism, Japan: 国土交通省 HP (in Japanese) 国土交通省 HP (in English) 国土交通省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)

Code for Japan / COVID-19 Japan: Code for Japan COVID-19 Japan Dashboard (CC BY 4.0) COVID-19 Japan 都道府県別 感染症病床数 (CC BY)

Wikipedia: Wikipedia

LinkData: LinkData (Public Domain)

Inspiration

1. Changes in number of cases over time
2. Percentage of patients without symptoms / mild or severe symptoms
3. What to do next to prevent outbreak

License and how to cite

Kindly cite this dataset under CC BY-4.0 license as follows. - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, GitHub repository, https://github.com/lisphilar/covid19-sir/data/japan, or - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, Kaggle Dataset, https://www.kaggle.com/lisphilar/covid19-dataset-in-japan

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

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19) was announced as an outbreak by the World Health Organization (WHO) in January 2020 and as a pandemic in March 2020. The majority of infected individuals have experienced no or only mild symptoms, ranging from fully asymptomatic cases to mild pneumonic disease. However, a minority of infected individuals develop severe respiratory symptoms. The objective of this study was to identify susceptible HLA alleles and clinical markers that can be used in risk prediction model for the early identification of severe COVID-19 among hospitalized COVID-19 patients. A total of 137 patients with mild COVID-19 (mCOVID-19) and 53 patients with severe COVID-19 (sCOVID-19) were recruited from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan for the period of February–August 2020. High-resolution sequencing-based typing for eight HLA genes was performed using next-generation sequencing. In the HLA association studies, HLA-A*11:01:01:01 [Pc = 0.013, OR = 2.26 (1.27–3.91)] and HLA-C*12:02:02:01-HLA-B*52:01:01:02 [Pc = 0.020, OR = 2.25 (1.24–3.92)] were found to be significantly associated with the severity of COVID-19. After multivariate analysis controlling for other confounding factors and comorbidities, HLA-A*11:01:01:01 [P = 3.34E-03, OR = 3.41 (1.50–7.73)], age at diagnosis [P = 1.29E-02, OR = 1.04 (1.01–1.07)] and sex at birth [P = 8.88E-03, OR = 2.92 (1.31–6.54)] remained significant. The area under the curve of the risk prediction model utilizing HLA-A*11:01:01:01, age at diagnosis, and sex at birth was 0.772, with sensitivity of 0.715 and specificity of 0.717. To the best of our knowledge, this is the first article that describes associations of HLA alleles with COVID-19 at the 4-field (highest) resolution level. Early identification of potential sCOVID-19 could help clinicians prioritize medical utility and significantly decrease mortality from COVID-19.

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.

Case data from 01-15-2020 to 08-16-2020, this data repository stores COVID-19 virus case data for Japan, including the daily case, summary data, and base map. Each zip file contains weekly case data from Monday to Sunday.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

Copyright 2020 by The New York Times Company

Coronavirus (Covid-19) Data in the United States

[ U.S. Data (Raw CSV) | U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

United States Data Data on cumulative coronavirus cases and deaths can be found in three files, one for each of these geographic levels: U.S., states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information. If a county is not listed for a date, then there were zero reported confirmed cases and deaths.

State and county files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

U.S. National-Level Data The daily number of cases and deaths nationwide, including states, U.S. territories and the District of Columbia, can be found in the us.csv file. (Raw CSV file here.)

date,cases,deaths 2020-01-21,1,0 ... State-Level Data State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths 2020-01-21,Washington,53,1,0 ... County-Level Data County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths 2020-01-21,Snohomish,Washington,53061,1,0 ... In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Methodology and Definitions The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial level have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match with the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their own records. And when officials in some states reported new cases without immediately identifying where the patients were being treated, we attempted to add informati...

Japan WHO: COVID-2019: Number of Patients: Confirmed: To-Date:Int.Conveyan(JP) data was reported at 764.000 Person in 24 Dec 2023. This stayed constant from the previous number of 764.000 Person for 23 Dec 2023. Japan WHO: COVID-2019: Number of Patients: Confirmed: To-Date:Int.Conveyan(JP) data is updated daily, averaging 764.000 Person from Feb 2020 (Median) to 24 Dec 2023, with 1419 observations. The data reached an all-time high of 764.000 Person in 24 Dec 2023 and a record low of 14.000 Person in 05 Feb 2020. Japan WHO: COVID-2019: Number of Patients: Confirmed: To-Date:Int.Conveyan(JP) data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued). Cases identified on a cruise ship currently in Japanese territorial waters 2. One case on Feb. 13 was reclassified as Japan.

Coronavirus (COVID-19) Data in the United States

[ U.S. State-Level Data (Raw CSV) | U.S. County-Level Data (Raw CSV) ]

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real-time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists, and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

United States Data

Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information.

Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

Download all the data or clone this repository by clicking the green "Clone or download" button above.

State-Level Data

State-level data can be found in the states.csv file. (Raw CSV file here.)

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...

County-Level Data

County-level data can be found in the counties.csv file. (Raw CSV file here.)

date,county,state,fips,cases,deaths
2020-01-21,Snohomish,Washington,53061,1,0
...

In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Methodology and Definitions

The data is the product of dozens of journalists working across several time zones to monitor news conferences, analyze data releases and seek clarification from public officials on how they categorize cases.

It is also a response to a fragmented American public health system in which overwhelmed public servants at the state, county and territorial levels have sometimes struggled to report information accurately, consistently and speedily. On several occasions, officials have corrected information hours or days after first reporting it. At times, cases have disappeared from a local government database, or officials have moved a patient first identified in one state or county to another, often with no explanation. In those instances, which have become more common as the number of cases has grown, our team has made every effort to update the data to reflect the most current, accurate information while ensuring that every known case is counted.

When the information is available, we count patients where they are being treated, not necessarily where they live.

In most instances, the process of recording cases has been straightforward. But because of the patchwork of reporting methods for this data across more than 50 state and territorial governments and hundreds of local health departments, our journalists sometimes had to make difficult interpretations about how to count and record cases.

For those reasons, our data will in some cases not exactly match the information reported by states and counties. Those differences include these cases: When the federal government arranged flights to the United States for Americans exposed to the coronavirus in China and Japan, our team recorded those cases in the states where the patients subsequently were treated, even though local health departments generally did not. When a resident of Florida died in Los Angeles, we recorded her death as having occurred in California rather than Florida, though officials in Florida counted her case in their...

On March 15, 2022, 2,578 cases of coronavirus disease (COVID-19) were confirmed in Tokyo Prefecture. The number peaked at around 16.9 thousand on January 31, 2022. Following the accelerated development of cases in the prefecture, the Tokyo prefectural government rose the alert status of the infection level to the highest out of four levels.

Government measures Since the outbreak of the disease in the nation in January 2020, the Japanese government has announced the state of emergency four times for respective prefectures. Tokyo Prefecture was one of the prefectures that were under the state of emergency all four times. To ease the strain on medical facilities, Tokyo prefectural government added about 1,000 beds for COVID-19 patients in private facilities such as sports centers. As of March 2022, over 7,200 beds were designated for patients with the disease in the prefecture.

Tokyo Olympics and Paralympics As a direct impact of COVID-19, the Tokyo 2020 Summer Olympic and Paralympic Games were postponed to 2021. Consequently, the games took place from July to September 2021, one year after the original plan. The games were held without any overseas audience, and initially anticipated economic growth from inbound tourism in the nation did not materialize. 

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

The coronavirus disease 2019 (COVID-19) has caused a serious disease burden and poses a tremendous public health challenge worldwide. Here, we report a comprehensive epidemiological and genomic analysis of SARS-CoV-2 from 63 patients in Niigata City, a medium-sized Japanese city, during the early phase of the pandemic, between February and May 2020. Among the 63 patients, 32 (51%) were female, with a mean (±standard deviation) age of 47.9 ± 22.3 years. Fever (65%, 41/63), malaise (51%, 32/63), and cough (35%, 22/63) were the most common clinical symptoms. The median Ct value after the onset of symptoms lowered within 9 days at 20.9 cycles (interquartile range, 17–26 cycles), but after 10 days, the median Ct value exceeded 30 cycles (p < 0.001). Of the 63 cases, 27 were distributed in the first epidemic wave and 33 in the second, and between the two waves, three cases from abroad were identified. The first wave was epidemiologically characterized by a single cluster related to indoor sports activity spread in closed settings, which included mixing indoors with families, relatives, and colleagues. The second wave showed more epidemiologically diversified events, with most index cases not related to each other. Almost all secondary cases were infected by droplets or aerosols from closed indoor settings, but at least two cases in the first wave were suspected to be contact infections. Results of the genomic analysis identified two possible clusters in Niigata City, the first of which was attributed to clade S (19B by Nexstrain clade) with a monophyletic group derived from the Wuhan prototype strain but that of the second wave was polyphyletic suggesting multiple introductions, and the clade was changed to GR (20B), which mainly spread in Europe in early 2020. These findings depict characteristics of SARS-CoV-2 transmission in the early stages in local community settings during February to May 2020 in Japan, and this integrated approach of epidemiological and genomic analysis may provide valuable information for public health policy decision-making for successful containment of chains of infection.

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 ---

Japan MHLW: COVID-19: PCR: Confirmed data was reported at 33,826,903.000 Person in 08 May 2023. This records an increase from the previous number of 33,817,576.000 Person for 07 May 2023. Japan MHLW: COVID-19: PCR: Confirmed data is updated daily, averaging 1,687,422.000 Person from Feb 2020 (Median) to 08 May 2023, with 1183 observations. The data reached an all-time high of 33,826,903.000 Person in 08 May 2023 and a record low of 25.000 Person in 07 Feb 2020. Japan MHLW: COVID-19: PCR: Confirmed data remains active status in CEIC and is reported by Ministry of Health, Labour and Welfare. The data is categorized under High Frequency Database’s Disease Outbreaks – Table JP.D001: Ministry of Health, Labour and Welfare: Coronavirus Disease 2019 (COVID-2019) (Discontinued). Discrepancies on some timepoints are due to the inclusion of unspecified positive cases.

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

indonesia https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

This Project Tycho dataset includes a CSV file with COVID-19 data reported in JAPAN: 2019-12-30 - 2021-07-31. It contains counts of cases and deaths. 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", "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.

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 ---

BackgroundAccording to a recent paper by Gelfand et al., COVID-19 infection and case mortality rates are closely connected to the strength of social norms: “Tighter” cultures that abide by strict social norms are more successful in combating the pandemic than “looser” cultures that are more permissive. However, countries with similar levels of cultural tightness exhibit big differences in mortality rates. We are investigating potential explanations for this fact. Using data from Germany and Japan—two “tight” countries with very different infection and mortality rates—we examined how differences in socio-demographic and other determinants explain differences in individual preventive attitudes and behaviors.MethodsWe compared preventive attitudes and behaviors in 2020 based on real-time representative survey data and used logit regression models to study how individual attitudes and behaviors are shaped by four sets of covariates: individual socio-demographics, health, personality, and regional-level controls. Employing Blinder-Oaxaca regression techniques, we quantified the extent to which differences in averages of the covariates between Japan and Germany explain the differences in the observed preventive attitudes and behaviors.ResultsIn Germany and Japan, similar proportions of the population supported mandatory vaccination, avoided travel, and avoided people with symptoms of a cold. In Germany, however, a significantly higher proportion washed their hands frequently and avoided crowds, physical contact, public transport, peak-hour shopping, and contact with the elderly. In Japan, a significantly higher proportion were willing to be vaccinated. We also show that attitudes and behaviors varied significantly more with covariates in Germany than in Japan. Differences in averages of the covariates contribute little to explaining the observed differences in preventive attitudes and behaviors between the two countries.ConclusionConsistent with tightness-looseness theory, the populations of Japan and Germany responded similarly to the pandemic. The observed differences in infection and fatality rates therefore cannot be explained by differences in behavior. The major difference in attitudes is the willingness to be vaccinated, which was much higher in Japan. Furthermore, the Japanese population behaved more uniformly across social groups than the German population. This difference in the degree of homogeneity has important implications for the effectiveness of policy measures during the pandemic.