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 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.
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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.
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.
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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
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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.
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Covid-19 is spreading in India at a very high rate. Recently, India witnessed the most number of positive cases in a day. We must do what we can to understand and defeat this deadly virus. Here is the data set I gathered from official 'Indian Ministry of Health' website updated on 15 June, 2020. I hope you find it useful. I will keep updating the data set on a regular basis.
PC: Photo by Fusion Medical Animation on Unsplash
State - Name of the State/ Union Territory Active Cases - Number of active cases in the State Cured/Migrated - Number of Cases Cured/ Migrated from the State Deaths - Number of deaths in the State due to Covid19 Total Confirmed Cases - Total number of confirmed cases in the State (Active + Cured + Deaths)
This dataset is collected from JHP updated GitHub profile till late March 2020. It contains time series data and other data of coordinates of India etc please refer to the files for understanding.
Dataset Name Entries Attributes Covid complete.csv 19220 Province/State, Country/Region, Latitude, Longitude, Confirmed, Death and Recovered. Covid cases in India.xlsx 25 states S.No., Name of State/UT, Total Confirmed cases (Indian National), Total confirmed cases (Foreign National), Cured and Death Indian Coordinates.xlsx 36 states/UT Name of State/UT, Latitude and Longitude Per day cases.csv 56 Date, Total case, New case and Days after surpassing 100 cases Time series confirmed global.csv 242 67 Time series deaths global.csv 242 67 Time series recovered global.csv 242 67
JHU GitHub: https://github.com/CSSEGISandData/COVID-19
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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.
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Time series data of Indian State and UT for Covid19.
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.
Govt of India: https://www.mohfw.gov.in https://www.covid19india.org/
Data can be used to see the pattern and prediction so that we can stop the spread of COVID19.
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.
This Data is related to the World Fight against the Infectious Disease COVID-19 (CoronaVirus).
This DataSet contains the World Data of Total Cases, Total Death, Total Tests and more by each Country and Continents.
This data is collected by Web Scraping. In this, I Scrap the data from the website Worldometers by writing the code in Python. For more, please Check the Code. Special Thanks to the Website Worldometers for providing such data. https://www.kaggle.com/samrat77/coronavirus-data-web-scraping
Inspired by all the others kagglers who are posting datasets and kernels on a daily bases.
The dataset consists of total cases, new cases, new death, total test per day, etc. This can be used to predict future covid cases in India.
Please give credit to this dataset if you download it.
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Seroprevalence of 67.6% is used with 765 million infectionsa from an age-adjusted population as of 14 Jun-6 Jul 2021 from the 4th nationwide serosurvey [6].
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.
http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/
This dataset contains latest Covid-19 India state-wise data as on December 05, 2021. This dataset can be used to analyze covid in India. This dataset is great for Exploratory Data Analysis
Covid Data : https://www.mygov.in/covid-19 Population Data : https://www.indiacensus.net/
https://www.kaggle.com/anandhuh/datasets Please appreciate the effort with an upvote 👍
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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.
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Analysis of ‘COVID-19 India dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dhamur/covid19-india-dataset on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This data set contains the data of covid-19 Conformed, Recovered and Deaths in India. This data is took from the non-governmental organization.
COVID19-India - The data from 31-Jan-2020 to 31-Oct-2021. Remaining data from
--- Original source retains full ownership of the source dataset ---
COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.
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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.
A word on the flaws of numbers like this
People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.
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.