India recorded 44983152 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, India reported 531794 Coronavirus Deaths. This dataset includes a chart with historical data for India Coronavirus Cases.

India reported almost 45 million cases of the coronavirus (COVID-19) as of October 20, 2023, with more than 44 million recoveries and about 532 thousand fatalities. The number of cases in the country had a decreasing trend in the past months.

Burden on the healthcare system

With the world's second largest population in addition to an even worse second wave of the coronavirus pandemic seems to be crushing an already inadequate healthcare system. Despite vast numbers being vaccinated, a new variant seemed to be affecting younger age groups this time around. The lack of ICU beds, black market sales of oxygen cylinders and drugs needed to treat COVID-19, as well as overworked crematoriums resorting to mass burials added to the woes of the country. Foreign aid was promised from various countries including the United States, France, Germany and the United Kingdom. Additionally, funding from the central government was expected to boost vaccine production.

Situation overview
Even though days in April 2021 saw record-breaking numbers compared to any other country worldwide, a nation-wide lockdown has not been implemented. The largest religious gathering - the Kumbh Mela, sacred to the Hindus, along with election rallies in certain states continue to be held. Some states and union territories including Maharashtra, Delhi, and Karnataka had issued curfews and lockdowns to try to curb the spread of infections.

India reported over 44 million confirmed cases of the coronavirus (COVID-19) as of October 20, 2023. The number of people infected with the virus was declining across the south Asian country.

What is the coronavirus?

COVID-19 is part of a large family of coronaviruses (CoV) that are transmitted from animals to people. The name COVID-19 is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged. Symptoms of COVID-19 resemble that of the common cold, with fever, coughing, and shortness of breath. However, serious infections can lead to pneumonia, multi-organ failure, severe acute respiratory syndrome, and even death, if appropriate medical help is not provided.

COVID-19 in India

India reported its first case of this coronavirus in late January 2020 in the southern state of Kerala. That led to a nation-wide lockdown between March and June that year to curb numbers from rising. After marginal success, the economy opened up leading to some recovery for the rest of 2020. In March 2021, however, the second wave hit the country causing record-breaking numbers of infections and deaths, crushing the healthcare system. The central government has been criticized for not taking action this time around, with "#ResignModi" trending on social media platforms in late April. The government's response was to block this line of content on the basis of fighting misinformation and reducing panic across the country.

The number of COVID-19 vaccination doses administered per 100 people in India rose to 156 as of Oct 27 2023. This dataset includes a chart with historical data for India Coronavirus Vaccination Rate.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Maharashtra confirmed over 8.1 million cases of the coronavirus (COVID-19) as of October 20, 2023, with over 148 thousand fatalities and over eight million recoveries. A state-wide lockdown was implemented in late April 2021 to attempt reducing infections and deaths.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Delhi confirmed almost two million cases of the coronavirus (COVID-19) as of October 20, 2023, with over 26 thousand fatalities and over two million recoveries. The Delhi government, led by Arvind Kejriwal and the Aam Aadmi Party implemented a night and weekend curfew to curb infection numbers in late April 2021. The capital region faced acute shortage of oxygen and ICU beds during this time period.

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.

COVID-19: As on Date: Number of Confirmed Cases: Kerala data was reported at 6,921,277.000 Case in 05 May 2025. This records an increase from the previous number of 6,921,265.000 Case for 28 Apr 2025. COVID-19: As on Date: Number of Confirmed Cases: Kerala data is updated daily, averaging 6,547,507.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 6,921,277.000 Case in 05 May 2025 and a record low of 22.000 Case in 15 Mar 2020. COVID-19: As on Date: Number of Confirmed Cases: Kerala data remains active status in CEIC and is reported by Ministry of Health and Family Welfare. The data is categorized under High Frequency Database’s Disease Outbreaks – Table IN.HLF006: Disease Outbreaks: Coronavirus 2019: MOHFW.

Dataset general description:

• This dataset reports 4195 recurrent neural network models, their settings, and their generated prediction csv files, graphs, and metadata files, for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 time-steps and 40 time-steps alternatives). The used code is developed by the author and located in the following online data repository link: http://dx.doi.org/10.17632/yp4d95pk7n.1

Dataset content:

• Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1194 generated models files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated model files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated model files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated model files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 358 models, which are a sample of 1001 models. Also, 1 model in control group for India. 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11.

• Settings and metadata for the above 3 categories: 1. Used settings in json files for reproducibility. 2. Metadata about training and prediction setup and accuracy in csv files.

Raw data source that was used to train the models:

• The used raw data for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University): https://github.com/CSSEGISandData/COVID-19

• The models were trained on these versions of the raw data: 1. Link till 2020-06-29 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. Link till 2020-06-13 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

License: This prediction Dataset is licensed under CC BY NC 3.0.

Notice and disclaimer: 1- This prediction Dataset is for scientific and research purposes only. 2- The generation of this Dataset complies with the terms of use of the publicly available raw data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/CSSEGISandData/COVID-19 and therefore, the author of the prediction Dataset disclaims any and all responsibility and warranties regarding the contents of used raw data, including but not limited to: the correctness, completeness, and any issues linked to third-party rights.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The impact of the coronavirus (COVID-19) pandemic had not only brought the global economy to a standstill but set the clock backwards on the developmental progress of several nations. While the rate of infection in India did not appear to be as high as in other countries, precautionary measures adopted dealt a severe blow to the country’s major industries - with the service sector bearing the largest brunt of estimated loss. Manufacturing made a swift recovery in the following months.

Impact of key industries

The loss incurred by enforcing a lockdown in the country was estimated at 26 billion U.S. dollars and a significant decline in GDP growth is also expected in the June quarter of 2020. With the imposition of restrictions on transportation worldwide, the trade sector also took a hit. Exports and imports saw a drastic decline in the country especially in the case of essential commodities such as petroleum, food crops, and coal, among others.

Effect on business in India

The growth rate of the automotive business in India was expected to be the most adversely affected followed by the power supply and IT sectors. Furthermore, many startups, small and medium enterprises in India expected to face issues of supply disruption and a decrease in demand. The effects of aid from the Narendra Modi-led government arguably did little to help in the face of a faltering economy.

Network of 35 papers and 76 citation links related to "Indian Clinical Trials on COVID-19: A Review of Clinical Trials Registry of India (CTRI)".

Network of 38 papers and 75 citation links related to "Factors associated with COVID-19 stigma during the onset of the global pandemic in India: A cross-sectional study".

Context

As global communities respond to COVID-19, we've heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps could be helpful as they make critical decisions to combat COVID-19.

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. (https://www.google.com/covid19/mobility/)

Content

The data contains aggregated and anonymised aggregated data per day for each country. For say accessing data for India - the files 2020_IN_Region_Mobility_Report.csv for 2020 data and 2021_IN_Region_Mobility_Report.csv. The aggregated data is not only present at country level, but also at States and district level - as given in sub_region_1 and sub_region_2.

Acknowledgements

This data from report published by Google. https://www.google.com/covid19/mobility/

Inspiration

Some Questions to answer

1. India is having its Second Wave and one of the major causes is considered to the election rallies held in different parts of the country. How does Mobility Impact the COVID Cases?

2. Comparing Mobility across different Countries

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A majority of the coronavirus (COVID-19) cases in India affected people between ages 31 and 40 years as of October 18, 2021. Of these, the highest share of deaths during the measured time period was observed in people under the age of 50 years.

The Indian state of Punjab reported the highest number of active coronavirus (COVID-19) cases of over one thousand cases as of October 20, 2023. Kerala and Karnataka followed, with relatively lower casualties. That day, there were a total of over 44 million confirmed infections across India.

Additional file 1. Master chart in Excel.