Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

On 6/16/2023 CDPH replaced the booster measures with a new “Up to Date” measure based on CDC’s new recommendations, replacing the primary series, boosted, and bivalent booster metrics The definition of “primary series complete” has not changed and is based on previous recommendations that CDC has since simplified. A person cannot complete their primary series with a single dose of an updated vaccine. Whereas the booster measures were calculated using the eligible population as the denominator, the new up to date measure uses the total estimated population. Please note that the rates for some groups may change since the up to date measure is calculated differently than the previous booster and bivalent measures.

This data is from the same source as the Vaccine Progress Dashboard at https://covid19.ca.gov/vaccination-progress-data/ which summarizes vaccination data at the county level by county of residence. Where county of residence was not reported in a vaccination record, the county of provider that vaccinated the resident is included. This applies to less than 1% of vaccination records. The sum of county-level vaccinations does not equal statewide total vaccinations due to out-of-state residents vaccinated in California.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

Totals for the Vaccine Progress Dashboard and this dataset may not match, as the Dashboard totals doses by Report Date and this dataset totals doses by Administration Date. Dose numbers may also change for a particular Administration Date as data is updated.

Previous updates:

• On March 3, 2023, with the release of HPI 3.0 in 2022, the previous equity scores have been updated to reflect more recent community survey information. This change represents an improvement to the way CDPH monitors health equity by using the latest and most accurate community data available. The HPI uses a collection of data sources and indicators to calculate a measure of community conditions ranging from the most to the least healthy based on economic, housing, and environmental measures.

• Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 16+ and age 5+ denominators have been uploaded as archived tables.

• Starting on May 29, 2021 the methodology for calculating on-hand inventory in the shipped/delivered/on-hand dataset has changed. Please see the accompanying data dictionary for details. In addition, this dataset is now down to the ZIP code level.

The number of COVID-19 vaccination doses administered per 100 people in India rose to 156 as of Oct 27 2023. This dataset includes a chart with historical data for India Coronavirus Vaccination Rate.

About

Community collected, cleaned and organized COVID-19 datasets about India sourced from different government websites which are freely available to all. Here we have digitized them, so it can be used by all the researchers and students.

Infected Data

Column Discription

Main file in this dataset is COVID-19_India_Data.csv and the detailed descriptions are below.

Date_reported : Date of the observation in YYYY-MM-DD
cum_cases : Cumulative number of confirmed cases till that date
cum_death : Cumulative number of deaths till that date
cum_recovered : Cumulative number of recovered patients till that date
new_recovered : Daily new recovery
new_cases : New confirmed cases. Calculated by: current cum_cases - previous cum_case
new_death : New confirmed deaths. Calculated by: current cum_death - previous cum_death
cum_active_cases : Cumulative number of infected person till that date. Calculated by: cum_cases - cum_death - cum_recovered

Vaccination data

Column Discription

Main file in this dataset is Vaccination.csv and the detailed descriptions are below.

• date: date of the observation.
• total_vaccinations: total number of doses administered. For vaccines that require multiple doses, each individual dose is counted. If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. If they receive a third/booster dose, it goes up by again.
• people_vaccinated: total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same.
• people_fully_vaccinated: total number of people who received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1.
• daily_vaccinations_raw: daily change in the total number of doses administered. It is only calculated for consecutive days. This is a raw measure provided for data checks and transparency, but we strongly recommend that any analysis on daily vaccination rates be conducted using daily_vaccinations instead.
• daily_vaccinations: new doses administered per day (7-day smoothed). For countries that don't report data on a daily basis, we assume that doses changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window. An example of how we perform this calculation can be found here.
• total_vaccinations_per_hundred: total_vaccinations per 100 people in the total population of the country.
• people_vaccinated_per_hundred: people_vaccinated per 100 people in the total population of the country.

Acknowledgements

• Johns Hopkins University for making the data available for educational and academic research purposes.
• World Health Organization (WHO): https://www.who.int/
• Government of India: https://www.mygov.in/covid-19, Ministry of Health and Family Welfare: https://www.mohfw.gov.in/
• Our World in Data: https://ourworldindata.org/covid-vaccinations?country=~IND
• https://data.covid19india.org/

Content

The table has data about total number of doses administered and number of people who received a single and both the doses.

Inspiration

To Answer the question if vaccination is helping in reducing the number of daily cases

The number of COVID-19 vaccination doses administered in India rose to 2206672631 as of Oct 27 2023. This dataset includes a chart with historical data for India Coronavirus Vaccination Total.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

Context

Most of the datasets about covid19 vaccination data on Kaggle are not available citywise. So here I am to your rescue!! This version is just a starter with data for 9 cities. I plan to upload data for almost every city in India in the upcoming versions.

Vaccination planning has been a challenge in India. Earlier in the year, individual Indian citizens had to register on the Cowin or Aarogya Setu portal in order to receive a COVID-19 vaccination. The limited number of vaccination slots resulted in fewer administrations during the initial 5 months of the vaccination programme (phase 1–4). The Government of India has now amended the vaccination policy by waiving the preregistration requirement and offering free vaccinations to accelerate the programme. However, mass gatherings in healthcare settings might lead to a further surge in daily cases. Door-to-door vaccination might be a feasible and safe solution to avoid such assemblies.

Attribute Information

|: Date column. Contains date from 26 April,2020 to 31st Oct, 2021. || : Contains info about two variants of COVID: delta and delta7(delta7 is delta+ actually) ||_confirmed: Cases confirmed ||_deceased: Number of deaths reported ||_recovered: Cases recovered ||_tested: Number of people tested ||_vaccinated1: 1st dose of vaccine administered ||_vaccinated2: 2nd dose of vaccine administered |_total_confirmed: this column does not carry any information(did not remove it to maintain the originality of data) |_total_deceased: this column does not carry any information(did not remove it to maintain the originality of data) |_total_recovered: this column does not carry any information(did not remove it to maintain the originality of data)

More about the data

There are many NaN values in the data. They are not there because there is some error in the data. Vaccination dri drive started in India from Jan, 2021. So data for vaccination will be available from Jan,2021.

I am planning to upload the data for more cities in upcoming versions. If you want data of some specific city in India, ask for it in the discussion.

Despite COVID-19 vaccines being available to pregnant women in India since summer 2021, little is known about vaccine uptake among this high-need population. We conducted mixed methods research with pregnant and recently delivered rural women in northern India, consisting of 300 phone surveys and 15 in-depth interviews, in November 2021. Only about a third of respondents were vaccinated, however, about half of unvaccinated respondents reported that they would get vaccinated now if they could. Fears of harm to the unborn baby or young infant were common (22% of unvaccinated women). However, among unvaccinated women who wanted to get vaccinated, the most common barrier reported was that their healthcare provider refused to provide them with the vaccine. Gender barriers and social norms also played a role, with family members restricting women’s access. Trust in the health system was high, however, women were most often getting information about COVID-19 vaccines from sources that they did not trust, and they knew they were getting potentially poor-quality information. Qualitative data shed light on the barriers women faced from their family and healthcare providers but described how as more people got the vaccine, that norms were changing. These findings highlight how pregnant women in India have lower vaccination rates than the general population, and while vaccine hesitancy does play a role, structural barriers from the healthcare system also limit access to vaccines. Interventions must be developed that target household decision-makers and health providers at the community level, and that take advantage of the trust that rural women already have in their healthcare providers and the government. It is essential to think beyond vaccine hesitancy and think at the system level when addressing this missed opportunity to vaccinate high-risk pregnant women in this setting.

Despite the significant success of India’s COVID-19 vaccination program, a sizeable proportion of the adult population remains unvaccinated or has received a single dose of the vaccine. Despite the recommendations of the Government of India for the two doses of the COVID-19 vaccine and the precautionary booster dose, many people were still hesitant towards the COVID-19 full vaccination. Hence, this study aimed to identify the primary behavioral and psychological factors contributing to vaccine hesitancy. Cross-sectional data was collected via a multi-stage sampling design by using a scheduled sample survey in the Gorakhpur district of Uttar Pradesh, India, between 15 July 2022 to 30 September 2022. This study has utilized three health behavior models—the Health Belief Model (HBM), the Theory of Planned Behavior (TPB), and the 5C Psychological Antecedents of vaccination, and employed bivariate and multivariable binary logistic regression model to assess the level of vaccine hesitancy and predictive health behavior of the respondents. Results indicate that among the constructs of the HBM and 5C Antecedents models, "perceived benefits", "confidence" and "collective responsibility" showed a lesser likelihood of COVID-19 vaccine hesitancy. However, in the TPB model constructs, a ‘negative attitude towards the vaccine’ showed a four times higher likelihood of COVID-19 vaccine hesitancy. From the future policy perspective, this study suggested that addressing the issue of ‘negative attitudes towards the vaccine’ and increasing the trust or confidence for the vaccine through increasing awareness about the benefits of the vaccination in India may reduce vaccine hesitancy.

Note: In these datasets, a person is defined as up to date if they have received at least one dose of an updated COVID-19 vaccine. The Centers for Disease Control and Prevention (CDC) recommends that certain groups, including adults ages 65 years and older, receive additional doses.

Starting on July 13, 2022, the denominator for calculating vaccine coverage has been changed from age 5+ to all ages to reflect new vaccine eligibility criteria. Previously the denominator was changed from age 16+ to age 12+ on May 18, 2021, then changed from age 12+ to age 5+ on November 10, 2021, to reflect previous changes in vaccine eligibility criteria. The previous datasets based on age 12+ and age 5+ denominators have been uploaded as archived tables.

Starting June 30, 2021, the dataset has been reconfigured so that all updates are appended to one dataset to make it easier for API and other interfaces. In addition, historical data has been extended back to January 5, 2021.

This dataset shows full, partial, and at least 1 dose coverage rates by zip code tabulation area (ZCTA) for the state of California. Data sources include the California Immunization Registry and the American Community Survey’s 2015-2019 5-Year data.

This is the data table for the LHJ Vaccine Equity Performance dashboard. However, this data table also includes ZTCAs that do not have a VEM score.

This dataset also includes Vaccine Equity Metric score quartiles (when applicable), which combine the Public Health Alliance of Southern California’s Healthy Places Index (HPI) measure with CDPH-derived scores to estimate factors that impact health, like income, education, and access to health care. ZTCAs range from less healthy community conditions in Quartile 1 to more healthy community conditions in Quartile 4.

The Vaccine Equity Metric is for weekly vaccination allocation and reporting purposes only. CDPH-derived quartiles should not be considered as indicative of the HPI score for these zip codes. CDPH-derived quartiles were assigned to zip codes excluded from the HPI score produced by the Public Health Alliance of Southern California due to concerns with statistical reliability and validity in populations smaller than 1,500 or where more than 50% of the population resides in a group setting.

These data do not include doses administered by the following federal agencies who received vaccine allocated directly from CDC: Indian Health Service, Veterans Health Administration, Department of Defense, and the Federal Bureau of Prisons.

For some ZTCAs, vaccination coverage may exceed 100%. This may be a result of many people from outside the county coming to that ZTCA to get their vaccine and providers reporting the county of administration as the county of residence, and/or the DOF estimates of the population in that ZTCA are too low. Please note that population numbers provided by DOF are projections and so may not be accurate, especially given unprecedented shifts in population as a result of the pandemic.

Context

Sate-wise Information On COVID-19 Vaccine Doses.

Content

Dataset : COVID-19 Statewise Vaccine Latest Dataset - India.csv

It Contains:

STATE / UNION TERRITORY -Name of the State/UTS.

TOTAL VACCINATION DOSES -Total number of vaccination doses distributed.

DOSE1 -Number of people received Dose 1.

DOSE2 -Number of people received Dose 2.

VACCINATION DOSES DAY BEFORE -Total number of vaccination doses distributed the day before.

Acknowledgements

https://www.mygov.in/covid-19

Context

As India recently Crossed the milestone of Administering 1 Billion Covid Doses. So here is the DataSet which provides the Figures of covid 19 doses state-wise.

Content

The DATA SET provides the information about the pace at which Covid - 19 Doses (both Dose 1 and 2) administered In India Statewise.

Acknowledgements

I would like to thank Goverment Of India for doing such a Fantastic Job of Driving a National Wide Campaign Of Administering Free Covid-19 Doses through out country.

Inspiration

Any Hidden Pattern

Context

The raw state-wise and district-wise covid19 vaccination data published by covid19india.org.

Content

The data is downloaded from Covid19India.org and consist of three CSV files. - 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.csv : Number of vaccine doses administered statewise

Acknowledgements

Special thanks 🙏 to the Covid19India.org team for their data-rich website and API.

Inspiration

• In each state of India, how many are vaccinated each day?
• How many are vaccinated in each district of a state as per the CoWin database?

Factors associated with being vaccinated among pregnant and postpartum women.

The impact of vaccine hesitancy on childhood immunization in low- and middle-income countries remains largely uncharacterized. This study describes the sociodemographic patterns of vaccine hesitancy in Chandigarh, India. Mothers of children 97% of mothers thought childhood vaccines were important, effective, and were a good way to protect against disease. However, many preferred their child to receive fewer co-administered vaccines (69%), and were concerned about side effects (39%). Compared to the “other caste” group, scheduled castes or scheduled tribes had 3.48 times greater odds of vaccine hesitancy (95% CI: 1.52, 7.99). Those with a high school education had 0.10 times the odds of vaccine hesitancy compared to those with less education (95% CI: 0.02, 0.61). Finally, those having more antenatal care visits were less vaccine hesitant (≥4 vs.

Demographics of the population.

Dataset

This dataset was created by Venkatesh

Contents

Vaccination practices and beliefs.

BackgroundIn 2005, India established a conditional cash transfer program called Janani Suraksha Yojana (JSY), to increase institutional delivery and encourage the use of reproductive and child health-related services.ObjectiveTo assess the effect of maternal receipt of financial assistance from JSY on childhood immunizations, post-partum care, breastfeeding practices, and care-seeking behaviors.MethodsWe use data from the latest district-level household survey (2007–2008) to conduct a propensity score matching analysis with logistic regression. We conduct the analyses at the national level as well as separately across groups of states classified as high-focus and non-high-focus. We carry out several sensitivity analyses including a subgroup analysis stratified by possession of an immunization card.ResultsReceipt of financial assistance from JSY led to an increase in immunization rates ranging from 3.1 (95%CI 2.2–4.0) percentage points for one dose of polio vaccine to 9.1 (95%CI 7.5–10.7) percentage points in the proportion of fully vaccinated children. Our findings also indicate JSY led to increased post-partum check-up rates and healthy early breastfeeding practices around the time of childbirth. No effect of JSY was found on exclusive breastfeeding practices and care-seeking behaviors. Effect sizes were consistently larger in states identified as being a key focus for the program. In an analysis stratified by possession of an immunization card, there was little to no effect of JSY among those with vaccination cards, while the effect size was much larger than the base case results for those missing vaccination cards, across nearly all immunization outcomes.ConclusionsEarly results suggest the JSY program led to a significant increase in childhood immunization rates and some healthy reproductive health behaviors, but the structuring of financial incentives to pregnant women and health workers warrants further review. Causal interpretation of our results relies on the assumption that propensity scores balance unobservable characteristics.

Context

• First case of COVID-19 in India was announced on 27th January 2020 in Thrissur Kerala. After October 2020, there was some relief due to the subsiding of the curve of the covid case.
• In January 2020, the Government of India approved two vaccines i.e. Astra-Zeneca's Vaccine produced by Serum Institute - Covidsheild and Bharat Biotech's indigenous vaccine - Covaxin.
• This dataset deals with these vaccinations in India and provides insights into them. The two files provide information about daily COVID-19 vaccinations throughout the country and total vaccinations in each state.
• The data was collected from Our World in Data Github repo for covid-19 and Government of India website containing Vaccinations State Data, merged with relevant attributes, and uploaded.
• Government of India also approved Russia's Sputnik V for emergency use authorization on 12th April 2021.

Content

Covid-19_India_Daywise_Vaccinations.csv

Columns: - location- Location of the vaccination(country). - date- Date in format dd-mm-yyyy. - vaccine- Name of the vaccine(s) administered in the country on that day. - source_url- Source of the information for the vaccination. - total-vaccinations- Total number of doses administered till that day. If a person receives one dose of the vaccine, this metric goes up by 1. If they receive a second dose, it goes up by 1 again. - total_vaccinations_per_hundred- total_vaccinations per 100 people in the total population. - people_vaccinated- Total number of people who received at least one vaccine dose. If a person receives the first dose of a 2-dose vaccine, this metric goes up by 1. If they receive the second dose, the metric stays the same. - people_vaccinated_per_hundred- people_vaccinated per 100 people in the total population. - people_fully_vaccinated- Total number of people who received all doses prescribed by the vaccination protocol. If a person receives the first dose of a 2-dose vaccine, this metric stays the same. If they receive the second dose, the metric goes up by 1. - people_fully_vaccinated_per_hundred- people_fully_vaccinated per 100 people in the total population. - daily_vaccinations- New doses administered per day. - daily_vaccinations_per_million- daily_vaccinations per 1,000,000 people in the total population. - daily_change_in_vaccinations- Change in the number of doses administered (daily_vaccinations) from the previous day.

Covid-19_Statewise_Vaccination_India.csv

Columns: - State/Union Territory- Name of the State or Union Territory. - Population (2011 census)- Population of the State/UT based on 2011 census. - 1st dose- Number of first doses that were administered. - 2nd dose- Number of second doses that were administered. - Cumulative doses administered- Total number of doses administered till date. - Percentage of people given atleast one dose- Percent of the population of the state. - Percentage of people fully vaccinated- Percent of the population of the state.

Acknowledgements

I like to specify that I am only making available to Kagglers the data that is produced and maintained by Our World in Data through their Github repo, and also the Ministry of Health and Family Welfare Government of India which provide daily vaccine stats through their website. - Our World in Data Github Repo - Ministry of Health and Family Welfare Government of India

Inspiration

From this data, what you could do is: - Visualisations about the daily vaccination trends in the country. - Which state has the fastest pace in vaccination? - Prediction of future daily vaccinations in the country.