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.

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.

Japan MHLW: COVID-19: PCR: Confirmed: DM: Discharged: Nagano data was reported at 465,784.000 Person in 08 May 2023. This records an increase from the previous number of 465,565.000 Person for 07 May 2023. Japan MHLW: COVID-19: PCR: Confirmed: DM: Discharged: Nagano data is updated daily, averaging 8,710.000 Person from Mar 2020 (Median) to 08 May 2023, with 1142 observations. The data reached an all-time high of 465,784.000 Person in 08 May 2023 and a record low of 0.000 Person in 20 Mar 2020. Japan MHLW: COVID-19: PCR: Confirmed: DM: Discharged: Nagano 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).

COVID-19 data by prefecture in Japan.

Original data provided by https://github.com/kaz-ogiwara/covid19, under MIT license.

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.

Japan recorded 767275 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, Japan reported 18388 Coronavirus Deaths. This dataset includes a chart with historical data for Japan Coronavirus Recovered.

The number of COVID-19 vaccination doses administered in Japan rose to 383747738 as of Oct 27 2023. This dataset includes a chart with historical data for Japan Coronavirus Vaccination Total.

Sociodemographic characteristics and numbers of samples (n = 1856).

Goodness-of-fit indices for confirmatory factor analyses of BFS, DASS, and MFQ.

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

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.

IntroductionAddresing vaccine hesitancy is considered an important goal in management of the COVID-19 pandemic. We sought to understand what factors influenced people, especially those initially hesitant, to receive two or more vaccine doses within a year of the vaccine’s release.MethodsWe conducted longitudinal Web-based observational studies of 3,870 individuals. The surveys were conducted at four different time points: January 2021, June 2021, September 2021, and December 2021. In the baseline survey (January 2021), we assessed vaccination intention (i.e., “strongly agree” or “agree” [acceptance], “neutral” [not sure], and “disagree” or “strongly disagree” [hesitance]), and assumptions about coronavirus disease (COVID-19), COVID-19 vaccine, COVID-19-related health preventive behavior, and COVID-19 vaccine reliability. In subsequent surveys (December 2021), we assessed vaccination completion (i.e., ≥2 vaccinations). To investigate the relationship between predictors of COVID-19 vaccination completion, a multivariable logistic regression model was applied. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were calculated while adjusting for gender, age, marital status, presence of children, household income category, and presence of diseases under treatment. In a stratified analysis, predictors were determined based on vaccination intention.ResultsApproximately 96, 87, and 72% of those who demonstrated acceptance, were not sure, or hesitated had been vaccinated after 1 year, respectively. Overall, significant factors associated with COVID-19 vaccine compliance included the influence of others close to the index participant (social norms) (AOR, 1.80; 95% CI, 1.56–2.08; p < 0.001), vaccine confidence (AOR, 1.39; 95% CI, 1.18–1.64; p < 0.001) and structural constraints (no time, inconvenient location of medical institutions, and other related factors) (AOR, 0.80; 95% CI, 0.70–0.91; p = 0.001). In the group of individuals classified as hesitant, significant factors associated with COVID-19 vaccine compliance included social norms (AOR, 2.43; 95% CI, 1.83–3.22; p < 0.001), confidence (AOR, 1.44; 95% CI, 1.10–1.88; p = 0.008), and knowledge (AOR, 0.69; 95% CI, 0.53–0.88; p = 0.003).DiscussionWe found that dissemination of accurate information about vaccines and a reduction in structural barriers to the extent possible enhanced vaccination rates. Once the need for vaccination becomes widespread, it becomes a social norm, and further improvements in these rates can then be anticipated. Our findings may help enhance vaccine uptake in the future.

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

Significant differences (ps < .05) revealed by one-way ANOVA (n = 1856) with demographic characteristics as dependent variables.

Abstract (our paper)

The spread of COVID-19, the so-called new coronavirus, is currently having an enormous social and economic impact on the entire world. Under such a circumstance, the spread of information about the new coronavirus on SNS is having a significant impact on economic losses and social decision-making. In this study, we investigated how the new type of coronavirus has become a social topic in Japan, and how it has been discussed. In order to determine what kind of impact it had on people, we collected and analyzed Japanese tweets containing words related to the new corona on Twitter. First, we analyzed the bias of users who tweeted. As a result, it is clear that the bias of users who tweeted about the new coronavirus almost disappeared after February 28, 2020, when the new coronavirus landed in Japan and a state of emergency was declared in Hokkaido, and the new corona became a popular topic. Second, we analyzed the emotional words included in tweets to analyze how people feel about the new coronavirus. The results show that the occurrence of a particular social event can change the emotions expressed on social media.

Data

Tweets_YYYY-MM-DD.tsv.gz: The first column is the tweet id, the second column is the date and time (JST) when the tweet was posted, the third column is the flag as to whether the tweet was used for emotion analysis or not, and the fourth column is the tweet id of the retweet source. This data was collected by giving the query "新型肺炎 OR 武漢 OR コロナ OR ウイルス OR ウィルス" to the Twitter Search API. Therefore, most of the tweets are Japanese tweets. We conducted emotion analysis on tweets, excluding retweets and tweets containing links. The fourth column is empty if the tweet is not a retweet.

KL-Divergence.tsv.gz: The first column is the date (JST), and the second column is the value of KL-Divergence that calculated the bias of the users who posted tweets related to COVID-19. The value of KL-Divergence was calculated with all users appearing in Tweets_YYYY-MM-DD.tsv.gz. Based on the sampling stream data, we determined that if the value is below 0.6, there is no bias.

Emotions_by_ML-Ask.tsv.gz: The first column is the date (JST), the second and subsequent columns are the number of tweets for each emotion, and the last column is the number of tweets analyzed for the day. For this analysis, we only used tweets with a value of 1 in the third column of Tweets_YYYY-MM-DD.tsv.gz. We used pymlask (Python implementation of ML-Ask) to estimate the emotion of the tweet.

Publication

This data set was created for our study. If you make use of this data set, please cite: Fujio Toriumi, Takeshi Sakaki, Mitsuo Yoshida. Social Emotions Under the Spread of COVID-19 Using Social Media. Transactions of the Japanese Society for Artificial Intelligence (in Japanese). vol.35, no.4, pp.F-K45_1-7, 2020. 鳥海不二夫, 榊剛史, 吉田光男. ソーシャルメディアを用いた新型コロナ禍における感情変化の分析. 人工知能学会論文誌. vol.35, no.4, pp.F-K45_1-7, 2020. https://doi.org/10.1527/tjsai.F-K45

The novel coronavirus infectious disease (COVID-19) pandemic has negatively impacted not only our physical health but also mental health, including increasing depressive and anxiety symptoms. In particular, socially and physically vulnerable populations, such as people experiencing homelessness (PEH), may be more likely to have their mental health worsened by the pandemic due to having more difficulty meeting basic human needs. Therefore, this study aims to assess the impact of COVID-19 on mental health of the homeless in Japan by evaluating depressive and anxiety symptoms and identifying the associated factors particularly, sociodemographic variables as age, employment status and the fear and perceived risk of COVID-19 infection. A cross-sectional interview survey among 158 PEH in Osaka Prefecture was conducted from April to May 2022. The survey included sociodemographic questions and history and perceived risk of infection with COVID-19. Depressive symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9) and anxiety symptoms using the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the fear of COVID-19 using the seven-item Fear of New Coronavirus Scale (FCV-19S). In this study, the prevalence of depression (PHQ-9≥10) was 38.6%, anxiety disorder (GAD≥10) was 19.0%, and high fear of COVID-19 (FCV-19S≥19) was 28.5%. Univariate logistic regression analysis revealed that PEH in younger age groups (18–34 years), and with joblessness, higher perceived infection risk, and higher fear of COVID-19 were more likely to suffer from depression and anxiety (p

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.

This data is about...

Daily population estimates for the 23 wards of Tokyo for the period February 1 to April 6, 2020 2020年2月1日～4月6日までの東京23区の滞在人口推計値の日次推移データ

Source

yahoo data solutions ヤフー・データソリューション

Note

Currently this kaggle dataset have data up to April 6th, 2020. As the data may not be updated in the near future, it is recommended to use this kernel or any other web-scraping techniques to get the latest version of the data.

現在このKaggleデータセットには2020年4月6日までのデータしか入っていません。 データは近い将来更新されなくなる恐れがあるので、 このカーネルや他のウェブスクレイピング技術を用いて、最新のデータを取得することをおすすめします。

Japan MHLW: COVID-19: PCR: Confirmed: QA: Discharged data was reported at 21,939.000 Person in 27 Sep 2022. This records an increase from the previous number of 21,918.000 Person for 26 Sep 2022. Japan MHLW: COVID-19: PCR: Confirmed: QA: Discharged data is updated daily, averaging 2,990.500 Person from Mar 2020 (Median) to 27 Sep 2022, with 918 observations. The data reached an all-time high of 21,939.000 Person in 27 Sep 2022 and a record low of 1.000 Person in 05 Apr 2020. Japan MHLW: COVID-19: PCR: Confirmed: QA: Discharged 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). For April 26, 2021 data, the number of persons who were discharged from the airport / seaport quarantine or were canceled from medical treatment, 3 cases were added, so the total number is shown after subtracting 3 cases from the total number as of the previous day.