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.

Dataset

This dataset was created by JANANI D

Released under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

Contents

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.

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.

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%

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.

Context: COVID-19 Cases are on the rise in India. This dataset allows one to model the spread of COVID-19 in India.

Content: The data shows the number of total confirmed covid-19 cases per day in India.

Acknowledgement: This data comes from the Free COVID-19 API and can be found at https://documenter.getpostman.com/view/10808728/SzS8rjbc.

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.

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%

1) Data Introduction • The COVID-19 India Containment Zone Classification dataset categorizes Indian districts into Red, Orange, and Green Zones based on COVID-19 case metrics as of May 4. This classification aids in understanding the spread and control of COVID-19 across different regions.

2) Data Utilization (1) COVID-19 India Containment Zone data has characteristics that: • It includes detailed district-level information on the zone classification (Red, Orange, Green) based on COVID-19 metrics. This information is crucial for analyzing the spread of the virus, the effectiveness of containment measures, and for planning public health strategies. (2) COVID-19 India Containment Zone data can be used to: • Public Health Management: Assists in resource allocation, planning containment measures, and implementing targeted lockdowns based on zone classification. • Research and Analysis: Supports epidemiological studies, modeling the spread of the virus, and assessing the impact of containment measures in different zones.

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.

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.

Dataset

This dataset was created by Akshansh Gupta

Contents

Breathe India: Covid-19 Effect On Pollution
This dataset falls under the category Environmental Data Air Quality Data.
It contains the following data: The dataset contains air quality data and AQI (Air Quality Index) at hourly and daily level of various stations across multiple cities in India.
This dataset was scouted on 2022-02-26 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/parulpandey/breathe-india-covid-19-effect-on-pollution/data

According to a survey conducted to understand the impact of the coronavirus COVID-19 on Indian startups and SMEs, a majority of respondents stated that it would have an impact. About 30 percent of respondents felt that it would decrease demand for their products or services.

The country went into lockdown on March 25, 2020, the largest in the world, restricting 1.3 billion people.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

According to a survey among Indian urban youth about life in lockdown, a majority of respondents got their information about the coronavirus (COVID-19) from private media houses in April 2020. Over **** percent of male respondents obtained latest information from Twitter at that time about the pandemic.

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 ---

Dataset

This dataset was created by Venuu Maadhav

Contents