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

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.

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. 

Website

COVID-19 Daily updates

My Github

Profile Data Set

Data collected from

COVID19-India - The data from 31-Jan-2020 to 31-Oct-2021. Remaining data from

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

A COVID‑19 vaccine is a vaccine intended to provide acquired immunity against severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2), the virus that causes coronavirus disease 2019 (COVID‑19). This Dataset contains India's state-wise vaccination data on 9 August 2022. This dataset great for practicing Exploratory Data Analysis.

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.

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.

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India's 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

The samples for these surveys were drawn from surveys and impact evaluations previously conducted by the World Bank, the Ministry of Rural Development, India and IDInsight. A detailed note on the sampling frames is available for download.

Sampling deviation

Details will be made available after all rounds of data collection and analysis is complete.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaire consists of the following modules: - Module 0: Introduction - Module 1: Migration - Module 2: Labor and Income - Module 3: Consumption - Module 4: Agriculture - Module 5: Access to Relief - Module 6: Health

Response rate

~55%

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

Case data from 03-10-2020 to 08-16-2020, this data repository stores COVID-19 virus case data for India, including the daily case, summary data, and base map. Each zip file contains weekly case data from Monday to Sunday.

Analysis of ‘COVID-19 India Time Series’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ravichaubey1506/covid19-india on 28 January 2022.

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

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment.

Content

COVID-19 cases at daily level is present in covid_time_series.csv COVID-19 cases for different states till 1 may 2020 is present in covid_india_states.csv

Acknowledgements

Thanks to Indian Ministry of Health & Family Welfare for making the data available to general public.

Thanks to covid19india.org for making the individual level details and testing details available to general public.

Thanks to Wikipedia for population information.

Inspiration

Forecast for next 15 days and some EDA on Spread of Corona Virus

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

Dataset

This dataset was created by Chandan Kumar

Released under Database: Open Database, Contents: Database Contents

Contents

Abstract

An effective policy response to the economic impacts of the COVID-19 pandemic requires an enormous range of data to inform the design and response of programs. Public health measures require data on the spread of the disease, beliefs in the population, and capacity of the health system. Relief efforts depend on an understanding of hardships being faced by various segments of the population. Food policy requires measurement of agricultural production and hunger. In such a rapidly evolving pandemic, these data must be collected at a high frequency. Given the unexpected nature of the shock and urgency with which a response was required, Indian policymakers needed to formulate policies affecting India’s 1.4 billion people, without the detailed evidence required to construct effective programs. To help overcome this evidence gap, the World Bank, IDinsight, and the Development Data Lab sought to produce rigorous and responsive data for policymakers across six states in India: Jharkhand, Rajasthan, Uttar Pradesh, Andhra Pradesh, Bihar, and Madhya Pradesh.

Geographic coverage

Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

This dataset includes observations covering six states (Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, Uttar Pradesh) and three survey rounds. The survey did not have a single, unified frame from which to sample phone numbers. The final sample was assembled from several different sample frames, and the choice of frame sample frames varied across states and survey rounds.

These frames comprise four prior IDinsight projects and from an impact evaluation of the National Rural Livelihoods project conducted by the Ministry of Rural Development. Each of these surveys sought to represent distinct populations, and employed idiosyncratic sample designs and weighting schemes.

A detailed note covering key features of each sample frame is available for download.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The survey questionnaires covered the following subjects:

1. Agriculture: COVID-19-related changes in price realisation, acreage decisions, input expenditure, access to credit, access to fertilisers, etc.

2. Income and consumption: Changes in wage rates, employment duration, consumption expenditure, prices of essential commodities, status of food security etc.

3. Migration: Rates of in-migration, migrant income and employment status, return migration plans etc.

4. Access to relief: Access to in-kind, cash and workfare relief, quantities of relief received, and constraints on the access to relief.

5. Health: Access to health facilities and rates of foregone healthcare, knowledge of COVID-19 related symptoms and protective behaviours.

While a number of indicators were consistent across all three rounds, questions were added and removed as and when necessary to account for seasonal changes (i.e: in the agricultural cycle).

Response rate

Round 1: ~55% Round 2: ~46% Round 3: ~55%

India COVID-19: As on Date: Total Number of Active Cases data was reported at 35.000 Case in 05 May 2025. This records an increase from the previous number of 29.000 Case for 28 Apr 2025. India COVID-19: As on Date: Total Number of Active Cases data is updated daily, averaging 44,029.000 Case from Mar 2020 (Median) to 05 May 2025, with 1587 observations. The data reached an all-time high of 3,745,237.000 Case in 10 May 2021 and a record low of 1.000 Case in 24 Feb 2025. India COVID-19: As on Date: Total Number of Active Cases 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.

To control the spread of COVID-19 in India and to aid the efforts of the Ministry of Health and Family Welfare (MOHFW), the Population Council and other non-governmental organizations are conducting research to assess residents’ ability to follow sanitation and social distancing precautions under a countrywide lockdown. The Population Council COVID-19 study team is implementing rapid phone-based surveys to collect information on knowledge, attitudes and practices, as well as needs, among 2,041 young people (ages 19–23 years) and/or an adult household member, sampled from an existing prospective cohort study with a total sample size of 20,594 in Bihar (n=10,433) and Uttar Pradesh (n=10,161). Baseline was conducted from April 3–22; subsequent iterations of the survey are planned to be conducted on a monthly basis. Baseline findings on awareness of COVID-19 symptoms, perceived risk, awareness of and ability to carry out preventive behaviors, misconceptions, and fears will inform the development of government and other stakeholders’ interventions and/or strategies. We are committed to openly sharing the latest versions of the study description, questionnaires, de-identified or aggregated datasets, and preliminary results. Data and findings can also be shared with partners working on the COVID-19 response.

We examined the construct validity of a 28-item survey that was designed to measure inner wellbeing (i.e., individuals’ thoughts and feelings about what they can do and be; White et al., 2014) among individuals in (1) the Global South nation of India (n = 205), (2) the Global North nation of the United Kingdom (n = 392), and (3) the nation of Greece, which is not readily categorized as Global South or Global North (n = 354) during COVID lockdown. Using a series of multiple-group confirmatory factor analyses via LISREL 10.20 (Joreskog & Sorbom, 2019), we tested the hypothesis that a model specifying seven factors (i.e., economic confidence, agency/participation, social connections, close relationships, physical/mental health, competence/self-worth, and values/meaning as intercorrelated domains) would provide a significantly better fit to the correlational data than would a model specifying a one factor (i.e., unidimensional inner wellbeing).

India tested over 935 million samples for the coronavirus (COVID-19) as of October 20, 2023. The number of people infected with the virus was coming down across the south Asian country. The country was hit with a second wave in March 2021, leading to a collapse of the healthcare system.

In past 24 hours, India, Asia had 68 new cases, N/A deaths and N/A recoveries.

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.

please visit the primary dataset page.Announcements:

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 28 January 2022.

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

About

This dataset contains latest Covid-19 India state-wise data as on January 13, 2022. 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
9. Population - Population of State/UT

Source

Covid Data : https://www.mygov.in/covid-19 Population Data : https://www.indiacensus.net/

Other Updated Covid Datasets

https://www.kaggle.com/anandhuh/datasets Please appreciate the effort with an upvote 👍

Thank You

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