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. 

As of September 14, 2022, a cumulative total of approximately 3.1 million people in Tokyo Prefecture tested positive for the coronavirus (COVID-19). Among them, close to three thousand patients were still hospitalized, roughly three million patients were discharged already, and around six thousand patients passed away. Tokyo recorded an accelerated development of new cases per day again from January 2022 onwards.

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 |

Introduction: As of June 7, 2021, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread to more than 200 countries. The global number of reported cases is more than 172.9 million, with more than 3.7 million deaths, and the number of infected individuals is still growing rapidly. Consequently, events and activities around the world were canceled or postponed, and the preparation for sporting events were greatly challenged. Under such circumstances, about 11,000 athletes from ~206 countries are arriving in Tokyo for the 32nd Summer Olympic Games. Therefore, it is urgently necessary to assess the occurrence and spread risk of COVID-19 for the Games.Objectives: To explore effective prevention and control measures for COVID-19 in large international events through simulations of different interventions according to risk assessment.Methods: We used a random model to calculate the number of initial infected patients and used Poisson distribution to determine the number of initial infected patients based on the number of countries involved. Furthermore, to simulate the COVID-19 transmission, the susceptible-exposed-symptomatic-asymptomatic-recovered-hospitalized (SEIARH) model was established based on the susceptible-exposed-infectious-recovered (SEIR) mathematical model of epidemic diseases. According to risk assessment indicators produced by different scenarios of the simulated interventions, the risk of COVID-19 transmission in Tokyo Olympic Games was assessed.Results: The current COVID-19 prevention measures proposed by the Japan Olympic Committee need to be enhanced. And large-scale vaccination will effectively control the spread of COVID-19. When the protective efficacy of vaccines is 78.1% or 89.8%, and if the vaccination rate of athletes reaches 80%, an epidemic prevention barrier can be established.

BackgroundMobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture.MethodsIn Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion.ResultsIn the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time.ConclusionOur results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.

Time series data of the daily reported number of new positive cases of COVID-19 for the entire country of Japan from 16 January 2020 to 21 February 2023.

As of May 31, 2024, the number of bankruptcies in Japan directly related to the impact of the coronavirus disease (COVID-19) amounted to ***** cases in Tokyo Prefecture. In total, around *** thousand business enterprises in the country went bankrupt due to COVID-19.

Additional file 1.

Inflation Rate in Japan increased to 3 percent in October from 2.90 percent in September of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, over 20,300 people in Japan died of suicide. The overall number of suicides decreased after the unexpected upward trend, likely connected to the COVID-19 pandemic. Why Japanese men are more likely to die by suicide  When looking at suicide numbers by gender, Japanese men are more likely to commit suicide compared to women. Attitudes on traditional gender roles in Japan may have shifted in recent decades, but social change has since been slow. Men are still expected to focus on their careers and provide for the family. Hence, economic slumps are typically reflected in rising suicide figures among men, as failure to fulfill social expectations can lead to mental health issues, which in turn might trigger suicidal thoughts. As an example, the suicide figures increased only for men in 2009 as a result of the global banking crisis. Suicide resulting from work-related issues is also more common among men than among women. Stress and pressure at work pose health risks It has been determined over the past few decades that one of the primary issues facing Japanese workers that leads to self-harm is exhaustion. Occupational sudden mortality, known as "karoshi (death by overwork)" is a well-known phenomenon in Japanese society. Besides physical pressure, mental stress from the employment may cause karoshi. Suicide due to occupational stress or overwork is called "karojisatsu (overwork suicide)" in Japan.

In 2024, Japan reported 16.4 suicides per 100,000 inhabitants. The country's suicide rate resumed its downward trend after an unexpected surge in recent years, likely connected to the COVID-19 pandemic. What are the reasons behind Japan’s high suicide rates?  While the majority of suicides in Japan stemmed from health reasons, existential concerns and problems directly related to work also accounted for thousands of self-inflicted deaths in the past years. One of the most profound issues faced by employees in Japan leading to self-harm is exhaustion. “Karoshi,” or death by overwork, is a well-known phenomenon in Japanese society. In addition to physical fatigue, karoshi may be precipitated by mental stress resulting from employment. Occupational stress or overwork-induced suicide is referred to as “karojisatsu (overwork suicide)” in Japan. Which demographic groups are affected? Although *************** are frequently depicted as the most at-risk demographic for suicide in Japan, the increasing occurrence of suicides among the elderly people and schoolchildren is causing concern. Bullying, isolation, and the lack of a proficient mental healthcare system can be additional factors contributing to the country’s high suicide rates among all age groups.