As of April 13, 2024, India had the highest number of confirmed deaths due to the outbreak of the novel coronavirus in the Asia-Pacific region, with over 533 thousand deaths. Comparatively, Indonesia, which had the second highest number of coronavirus deaths in the Asia-Pacific region, recorded approximately 162 thousand COVID-19 related deaths as of April 13, 2024. Contrastingly, Bhutan had reported 21 deaths due to COVID-19 as of April 13, 2024.

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.

In October 2020, Tripura recorded the highest COVID-19 deaths per million people compared to to other states and Union territories with ** deaths. Uttarakhand followed with over ** deaths per million people.

Indicators such as case fatality and doubling time are used to measure the spread of the disease. The total deaths per million is considered to be a good indicator, to better measure and understand, the efficacy of the measures undertaken to control the spread of the virus. A slacked increase along with a fall in the number of new deaths per day is suggestive of a good control indicator.

• This dataset was created by web-scraping Ministry of Health and Family Welfare, India on a daily basis.
• Dataset has entries from April 01, 2020 to July 27, 2020.
• The Index is the Date (MM/DD) on which data was collected eg. 602 ~ 06/02 ~June 02.
• Each of the Three CSV Files contains data of Each State of India.

This dataset is a comprehensive collection of data related to the spread of COVID-19 in India. It captures the number of confirmed cases and deaths in each state and union territory of India from the first reported case in January 2020 to the present day. The dataset was created to provide an understanding of the extent of the COVID-19 pandemic in India. It is important because it allows researchers, policy-makers and citizens to gain insights into the various factors that may be driving the spread of the virus in different states and regions of India. It also provides valuable information for researchers trying to understand the dynamics of the pandemic in India.

This dataset is important because it allows us to understand the current situation of the pandemic in India and to monitor the progress of the virus in each state. It can also be used to measure the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus. The dataset is applicable to anyone interested in understanding the dynamics of the COVID-19 pandemic in India, such as policy-makers, researchers, citizens, NGOs and media. It can be used to gain insights into the current situation and to track the progress of the virus in each state. It can also be used to monitor the effectiveness of the strategies implemented by the Indian Government to contain the spread of the virus.

Overall, this dataset provides a comprehensive view of the COVID-19 pandemic in India. It is updated on a daily basis, and provides essential information that is useful for researchers, policy-makers and citizens. It is an invaluable resource that can be used to understand the dynamics of the virus and to monitor the progress of the virus in each state.

This Dataset contains day-wise cumulative total positive cases, active cases, recoveries and death statistics due to COVID-19 in India up to 10 June 2024

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.

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.

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.

Analysis of ‘Latest Covid-19 India Statewise Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/latest-covid19-india-statewise-data on 12 November 2021.

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

About

This dataset contains latest Covid-19 India state-wise data as on November 07, 2021. This dataset can be used to analyze covid in India. This dataset is great for Exploratory Data Analysis

Attribute Information

1. State/UTs - Names of Indian States and Union Territories.
2. Total Cases - Total number of confirmed cases
3. Active - Total number of active cases
4. Discharged - Total number of discharged cases
5. Deaths - Total number of deaths
6. Active Ratio (%) - Ratio of number of active cases to total cases
7. Discharge Ratio (%) - Ratio of number of discharged cases to total cases
8. Death Ratio (%) - Ratio of number of deaths to total cases

Source

Link : https://www.mygov.in/covid-19

Other Updated Covid Datasets

https://www.kaggle.com/anandhuh/datasets

Thank You

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

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. The NHGDP losses were evaluated using cost-of-illness approach. Future NHGDP losses were discounted at 3%. Excess death estimates by the World Health Organisation (WHO) and Global Burden of Disease (GBD) were used. Sensitivity analysis was carried out by varying discount rates and Average Age of Death (AAD). 21,532 deaths in West Bengal since 17th March 2020 till 31st December 2022 decreased the future NHGDP by $0.92 billion. Nearly 90% of loss was due to deaths occurring in above 30 years age-group. The majority of the loss was borne among the 46–60 years age-group. The NHGDP loss/death was $42,646, however, the average loss/death declined with a rise in age. The loss increased to $9.38 billion and $9.42 billion respectively based on GBD and WHO excess death estimates. The loss increased to $1.3 billion by considering the lower age of the interval as AAD. At 5% and 10% discount rates, the losses reduced to $0.769 billion and $0.549 billion respectively. Results from the study suggest that COVID-19 contributed to major economic loss in West Bengal. The mortality and morbidity caused by COVID-19, the substantial economic costs at individual and population levels in West Bengal, and probably across India and other countries, is another argument for better infection control strategies across the globe to end the impact of this epidemic. Methods Various open domains were used to gather data on COVID-19 deaths in West Bengal and the aforementioned estimates. Economic losses in terms of Non-Health Gross Domestic Product (NHGDP)among six age-group brackets viz. 0–15, 16–30, 31–45, 46–60, 61–75 and 75 and above were estimated to facilitate comparisons and to initiate advocacy for an increase in health investments against COVID-19. This study used midpoint age as the age of death for all the age brackets. The legal minimum age for working i.e., 15 years. A sensitivity analysis was conducted to determine the effect of age on the overall total NHGDP loss estimate. The model was re-estimated assuming an average age at death to be the starting age of each age-group bracket. Based on existing literature discounted rate of interest to measure the value of life is taken as 2.9%. As a sensitivity analysis, NHGDP loss has also been computed using 5% and 10% of discounted rates of interest.

This is that Dataset of covid-19 of Total deaths and Total Cases in 11 countries (Australia, United States, Indonesia, Pakistan, Bangladesh, Russia, United Kingdom, South Africa, Brazil, Italy and India) for comparison that how covid-19 impact these countries from 1st March 2020 to 1st March 2022, Monthly wise.

Data taken from WHO Website.

Data is based on accumulation means the cases of previous month are add to the new month and in the last row of Dataset contain the Total of all.

Context

Time series data of Indian State and UT for Covid19.

Content

Time Series Data for number of cases, deaths and cured cases in Indian States and UT.

3 files COVID19_Cured_Indian_States_UT COVID19_Deaths_Indian_States_UT COVID19_TotalCases_Indian_States_UT

Can be used for prediction.

Acknowledgements

Govt of India: https://www.mohfw.gov.in https://www.covid19india.org/

Inspiration

Data can be used to see the pattern and prediction so that we can stop the spread of COVID19.

India COVID-19: As on Date: Total Number of Death data was reported at 533,665.000 Case in 05 May 2025. This stayed constant from the previous number of 533,665.000 Case for 28 Apr 2025. India COVID-19: As on Date: Total Number of Death data is updated daily, averaging 524,260.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 533,665.000 Case in 05 May 2025 and a record low of 2.000 Case in 16 Mar 2020. India COVID-19: As on Date: Total Number of Death 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.

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.

This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".

Data

The data is organized in the form of tables.

hypothesis-test-data

This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.

* Region
* Male_Deaths - Number of male COVID-19 deaths in region.  
* Female_Deaths - Number of female COVID-19 deaths in region.  
* Male_cases - Number of male COVID-19 positive in region.
* Female_cases - Number of female COVID-19 positive in region.

lasso-covid19India

This table contains data used for analysis on cases throughout India.

Columns from COVID-19 India data

* State_Code  
* State  
* District  
* Confirmed  
* Active  
* Recovered  
* Deceased

Columns taken from NFHS data

* Sex_ratio_of_the_total_population_females_per_1000_males  
* Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

lasso-KA+TN-bulletin

This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.

Data from Media Bulletin

* District    
* Total_Positives  
* total_deaths
* male_deaths  
* female_deaths  
* Male_cases_in_data
* Female_cases_in_data

Calculated Data

* Estimated_Male_cases - Estimated male cases using total positives column and existing case data
* Estimated_Female_Cases - Estimated female cases using total positives column and existing case data  
* Male_Mortality - Estimated Male Cases / male_deaths
* Female_Mortality - Estimated Female Cases / female_deaths

Columns taken from NFHS data

* Sex_Ratio_females_every_1000_males
* State  Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

Code

The code is available at this Github Repository.

Dataset

This dataset was created by Advik Maniar

Contents

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.

About This dataset contains latest Covid-19 India state-wise data as on September 30, 2021. This dataset can be used to analyze covid in India. This dataset is great for Exploratory Data Analysis

Attribute Information 1. State/UTs - Names of Indian States and Union Territories. 2. Total Cases - Total number of confirmed cases 3. Active - Total number of active cases 4. Discharged - Total number of discharged cases 5. Deaths - Total number of deaths 6. Active Ratio (%) - Ratio of number of active cases to total cases 7. Discharge Ratio (%) - Ratio of number of discharged cases to total cases 8. Death Ratio (%) - Ratio of number of deaths to total cases

Data Source

Link: https://www.mygov.in/covid-19