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.

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.

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.

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.

India recorded 29427330 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, India reported 476135 Coronavirus Deaths. This dataset includes a chart with historical data for India Coronavirus Recovered.

COVID-19: As on Date: Number of Death: Karnataka data was reported at 40,411.000 Case in 05 May 2025. This stayed constant from the previous number of 40,411.000 Case for 28 Apr 2025. COVID-19: As on Date: Number of Death: Karnataka data is updated daily, averaging 40,105.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 40,411.000 Case in 05 May 2025 and a record low of 1.000 Case in 25 Mar 2020. COVID-19: As on Date: Number of Death: Karnataka 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.

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.

This study estimates the economic losses (GDP), particularly the impact of COVID-19 deaths on non-health components of GDP in West Bengal state. 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 a public database of reported deaths that have happened as a result of the lockdown. These include deaths due to starvation and financial distress, exhaustion, accidents during migration, lack or denial of medical care, suicides, police brutality, crimes, and alcohol-withdrawal. We also have a few deaths wherein more details are needed, or where conflicting reports emerge. We put such deaths in the ‘unclassified’ category. More details.

Content

The WHO coronavirus (COVID-19) dashboard presents official daily counts of COVID-19 cases, deaths and vaccine utilization reported by countries, territories and areas. Through this dashboard, we aim to provide a frequently updated data visualization, data dissemination and data exploration resource, while linking users to other useful and informative resources.

Caution must be taken when interpreting all data presented, and differences between information products published by WHO, national public health authorities, and other sources using different inclusion criteria and different data cut-off times are to be expected. While steps are taken to ensure accuracy and reliability, all data are subject to continuous verification and change. All counts are subject to variations in case detection, definitions, laboratory testing, vaccination strategy, and reporting strategies.

Acknowledgements

© World Health Organization 2020, All rights reserved.

WHO supports open access to the published output of its activities as a fundamental part of its mission and a public benefit to be encouraged wherever possible. Permission from WHO is not required for the use of the WHO coronavirus disease (COVID-19) dashboard material or data available for download. It is important to note that:

WHO publications cannot be used to promote or endorse products, services or any specific organization.

WHO logo cannot be used without written authorization from WHO.

WHO provides no warranty of any kind, either expressed or implied. In no event shall WHO be liable for damages arising from the use of WHO publications.

For further information, please visit WHO Copyright, Licencing and Permissions.

Citation: WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available online: https://covid19.who.int/

Inspiration

Daily cases start increasing suddenly just before the new year and there's a fear for the upcoming wave. Everybody starts to predict the peak cases in the 3rd wave and the date the peak will be reached. Assume you are in the 1st week of January 2022 and there's panic in the country, for the Omicron variant is said to be highly transmittable. Using your machine learning and deep learning skills, you have to create a model that predicts accurately the peak for the 3rd wave.

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].

India marks one COVID-19 death every 5 minutes

https://ichef.bbci.co.uk/news/976/cpsprodpb/11C98/production/_118165827_gettyimages-1232465340.jpg" alt="">

Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

https://s.yimg.com/ny/api/res/1.2/XhVWo4SOloJoXaQLrxxUIQ--/YXBwaWQ9aGlnaGxhbmRlcjt3PTk2MA--/https://s.yimg.com/os/creatr-uploaded-images/2021-04/8aa568f0-a3e0-11eb-8ff6-6b9a188e374a" alt="">

Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

Context

This is a Covid 19 data set for India. The data set is updated frequently and is analysed using tableau. Click on the link to visit the tableau story. Click each of the caption in the story to unveil its content.

https://public.tableau.com/profile/ambili.nair#!/vizhome/COVID19Indiastory/Indiastory?publish=yes

The first Covid 19 case in India was reported on 30th January 2020 in South Indian state of Kerala on a medical student who was pursuing the studies at Wuhan University, China. Two more students were found to be infected in Kerala in the consecutive days. The Kerala government was successful in containing the disease with its proactive measures back then. The second outbreak of Covid 19 in India started in the first week of March from various parts of India in various people who visited the foreign countries and in some of the tourists from different countries.

The tableau story consists of the following data analysis : 1. State-wise number of infected and number of death count in India map. Hover the mouse on each state in the India map to know the count. 2. Click on the next caption to know the state-wise number of confirmed, active, recovered and deceased cases in the form of bar chart. 3. The next caption takes you to the bar chart which shows the number of cases getting confirmed in India each day starting from January 30, 2020. 4. Next caption takes us to an analysis of the Mortality rate and the Recovery rate (in percentage) of each of the Indian state. We get an idea how hard each of the state is hit by the pandemic. 5. Next caption gives a detailed analysis of the state Kerala which has the mortality rate of 0.806% and the recovery rate of 74.4% as of now. Hover the mouse to know the count in each district. Don't forget to have a look at the line graph of 'number of active cases' in Kerala. It looks almost flattened ! As everyday we hear the increasing number of cases and deaths across the country, this graph may make you feel better...! 6. Finally the caption takes you to the statistics from the topmost district of Kerala - Kasaragod. The total number of cases reported is 179 at Kasaragod. The active number of cases is just 12 as of now... !!! Have a look at the statistics from Kasaragod and the story of 'Kasaragod model' as some of the national media in India call it !!!

Content

This data set consists of the following data: 1. state-wise statistics - Confirmed, Active, Recovered, Deceased cases 2. day-wise count of infected and deceased from various states 3. Statistics from Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 4. Statistics from Kasaragod district, Kerala - day-wise count of confirmed, Active, Recovered, Deceased cases 5. Count of confirmed cases from various districts of India

Acknowledgements

Ministry of Health and Family Welfare - India covid19india.org Wikipedia page - Covid 19 Pandemic India Govt. of Kerala dashboard - official Kerala Covid 19 statistics

Inspiration

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

COVID-19: As on Date: Number of Death: Maharashtra data was reported at 148,602.000 Case in 05 May 2025. This stayed constant from the previous number of 148,602.000 Case for 28 Apr 2025. COVID-19: As on Date: Number of Death: Maharashtra data is updated daily, averaging 147,855.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 148,602.000 Case in 05 May 2025 and a record low of 0.000 Case in 16 Mar 2020. COVID-19: As on Date: Number of Death: Maharashtra 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.

COVID-19: As on Date: Number of Death: Gujarat data was reported at 11,101.000 Case in 28 Apr 2025. This stayed constant from the previous number of 11,101.000 Case for 21 Apr 2025. COVID-19: As on Date: Number of Death: Gujarat data is updated daily, averaging 10,944.000 Case from Mar 2020 (Median) to 28 Apr 2025, with 1581 observations. The data reached an all-time high of 11,101.000 Case in 28 Apr 2025 and a record low of 0.000 Case in 21 Mar 2020. COVID-19: As on Date: Number of Death: Gujarat 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.

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.

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.

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

COVID-19: As on Date: Number of Death: Andhra Pradesh data was reported at 14,733.000 Case in 05 May 2025. This stayed constant from the previous number of 14,733.000 Case for 28 Apr 2025. COVID-19: As on Date: Number of Death: Andhra Pradesh data is updated daily, averaging 14,730.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 14,733.000 Case in 05 May 2025 and a record low of 0.000 Case in 01 Apr 2020. COVID-19: As on Date: Number of Death: Andhra Pradesh 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.