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.

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. 

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 16, 2022, there was a total of approximately 5.9 million confirmed cases of coronavirus disease (COVID-19) in Japan, with around 529 thousand people needing inpatient treatment.

Development of cases in Japan Generally, the increase of new COVID-19 cases recorded from January to March 2020 in Japan followed a slower trajectory as compared to, for example, China, Europe, or the United States of America. The first reported case of COVID-19 in Japan was confirmed on January 16, 2020, when a man that had returned from Wuhan city, China, was tested positive. The first transmission within Japan was recorded on January 28. The number of new cases then increased tenfold in February. April saw a further acceleration of the infection rate. Consequently, the Japanese government declared a nationwide state of emergency that month. The government announced a state of emergency for the second time in January 2021, the third time in April 2021, and the forth time in the July 2021.

Vaccine rollout The Japanese government started the distribution of COVID-19 vaccination in February 2021, mainly for medical professionals. The administration of vaccination for general citizens commenced in April for senior citizens. The vaccine rate of the population was just over 74.7 percent for second doses as of March 2022.

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

About Datasets

Tokyo is the largest prefecture and has the largest number of cases of COVID-19 in Japan. The number of total confirmed cases in Tokyo is about 73000 (as of January 9th, 2021). In this dataset, data about COVID-19 in Tokyo contain. If you want to download it, please consider upvoting.

Data Source

Data was collected from Tokyo Metropolitan Government Open Data Catalog Site and Updates on COVID-19 in Tokyo.

Columns

tokyo_covid19_patients.csv file in this dataset has 7 columns. | Column | Description | | --- | --- | | Number | | | Date | Published Date | | Date (Onset) | Date of onset of symptoms | | Region | Region where patients live in | | Age | Patients age| | Gender | Patients gender| | Situation | This columns shows whether the patient was discharged (include death) or not.|

tokyo_cases_byarea.csv has 4 columns. | Column | Description | | --- | --- | | Area | This column shows that which area the municipality belong. | | Municipality | Municipality name | | Positive Cases | The number of total cases | | Code | Code required to draw a choropleth map |

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.

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.

GWAS genotype data of 2,393 Japanese COVID-19 cases.

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

As of April 17 in 2020, the highest number of coronavirus disease (COVID-19) patients in Japan was recorded among women aged 20 to 29 years old, at a share of approximately 20 percent. The biggest difference between gender could be seen in this age group as well as among patients aged 40 to 49 years old.

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

The LOS of COVID-19 cases (day).

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.

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

As of March 18, 2022, there was a total number of nearly six million confirmed cases of coronavirus disease (COVID-19) from patients living or staying in Japan. More than 12.6 thousand people were confirmed with the virus at the airport quarantine as of the same day. A total of almost six million confirmed cases were reported in Japan so far.

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

Time series data of the daily reported number of COVID-19 cases for all 47 prefectures in Japan from 1 February 2022 to 8 May 2023 (714 data points).

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

Please, If you enjoyed this dataset, don't forget to upvote it.

Content

From Novel Corona Virus 2019 Dataset:

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 cases in Brazil. Please note that this is a time series data and so the number of cases on any given day is a cumulative number.

The data is available from Jan/30/2020, when the first suspect case appeared in Brazil.

Acknowledgements

1. Avisos/Advertising - Please, before start working with this data, take a break to read this discussion.
2. Plataforma COVID Brazil - Public platform to share informations about the COVID-19 cases in Brazil.

Country level datasets

If you are interested in know about another country, please follow these Kaggle datasets:

• Novel Corona Virus 2019 Dataset - https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset
• India - https://www.kaggle.com/sudalairajkumar/covid19-in-india
• South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset
• Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy
• USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa
• Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland
• Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases
• Turkey - https://www.kaggle.com/gkhan496/covid19-in-turkey
• 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

Inspiration

1. Changes in number of cases over time
2. Change in cases over time at state level

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current coronavirus disease 2019 (COVID-19) pandemic and associated respiratory infections, has been detected in the feces of patients. Therefore, determining SARS-CoV-2 RNA levels in sewage may help to predict the number of infected people within the area. In this study, we quantified SARS-CoV-2 RNA copy number using reverse transcription quantitative real-time PCR with primers and probes targeting the N gene, which allows the detection of both wild-type and variant strain of SARS-CoV-2 in sewage samples from two wastewater treatment plants (WWTPs) in Kobe City, Japan, during the fourth and fifth pandemic waves of COVID-19 between February 2021 and October 2021. The wastewater samples were concentrated via centrifugation, yielding a pelleted solid fraction and a supernatant, which was subjected to polyethylene glycol (PEG) precipitation. The SARS-CoV-2 RNA was significantly and frequently detected in the solid fraction than in the PEG-precipitated fraction. In addition, the copy number in the solid fraction was highly correlated with the number of COVID-19 cases in the WWTP basin (WWTP-A: r = 0.8205, p 

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.

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...