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.

Context

This dataset provides a timeseries of COVID-19 reported cases including cured/migrated information of states of India. The data is available from 30th January 2020 onwards.

Content

The data is in CSV format and has 5 columns.

Date: Date in DD-MM-YYYY format State: Name if the state Total Confirmed Cases: Total number of confirmed cases as on Date Cured/Discharged/Migrated: Total number of cured, discharged or migrated cases as on Date Death: Total number of deaths as on Date

All figures are cumulative.

Acknowledgements

This dataset is created and maintained using the data available in public domain. The state-wise COVID-19 cases in India are published by Ministry of Health and Family Welfare, Government of India on their website https://www.mohfw.gov.in/. A snapshot of the data on the above website is taken at 11PM IST(UTC+05.30) daily and appended to this dataset. Part of the data for initial period is taken from India Today COVID-19 Tracker at https://www.indiatoday.in/india/story/coronavirus-cases-in-india-covid19-states-cities-affected-1653852-2020-03-09.

Banner Photo by Martin Sanchez on Unsplash

Context

I am an individual who is passionate about Datascience, thought of creating a dataset which can be updated as per the government website.The code fetches the latest data from the website and updates in the csv file

content Gives you the data details of Statewise all over India,Maintaining a daily wise dataset also from 14th april.

** Acknowledgements** Thanks MohFw for updating the data on daily basis

**Inspiration Looking for data suggestions **Just got a thought of how doctors and Police dept is working day and night,Just a little contribution from my side,not worried about the views

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.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.

Context

This dataset captures state and district wise COVID vaccination in India

Content

• Vaccination Data
  • cowin_vaccine_data_districtwise.csv: Key data points from CoWin database at a district level
  • cowin_vaccine_data_statewise.csv: Key data points from CoWin database at a state level
  • vaccine_doses_statewise_v2.csv: Number of vaccine doses administered statewise - Collected from MOHFW daily bulletin
• State Level Population Data
  • india_state_wise_projected_population_2021.csv: State wise projected population of India by 2021
• District Level Population Data
  • punjab_districtwise_estimated_population_2021.csv: District wise population for Punjab
  • karnataka_districtwise_estimated_population_2021.csv: District wise population for Karnataka
  • assam_districtwise_estimated_population_2021.csv: District wise population for Assam
  • tripura_districtwise_estimated_population_2021.csv: District wise population for Tripura
  • manipur_districtwise_estimated_population_2021.csv: District wise population for Manipur
  • mizoram_districtwise_estimated_population_2021.csv: District wise population for Mizoram
  • meghalaya_districtwise_estimated_population_2021.csv: District wise population for Meghalaya
  • nagaland_districtwise_estimated_population_2021.csv: District wise population for Nagaland
  • arunachal_pradesh_districtwise_estimated_population_2021.csv: District wise population for Arunachal Pradesh

Acknowledgements

Vaccination related three files have been captured fromcovid19india.org](https://www.covid19india.org/). CSV files are available for direct download at api.covid19india.org.

State wise projected population of India (in 2021) is collected from uidai.gov.in.

District wise projected population (in 2021) is collected from indiacensus.net.

Cover Image Credit: Ivan Diaz Unplash

Inspiration

This dataset can be used to track vaccination done at the state and district level.