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/

From the project page: https://covid19.uis.unesco.org/global-monitoring-school-closures-covid19/

The COVID-19 crisis has significantly affected the education sector across all regions. The closing of schools has interrupted the functioning of the system, reducing student learning, and restricting the activities of education authorities, parents, and decision-makers. As the pandemic progresses, many important decisions need to be made based on a systematic understanding of deployed policies up to date.

UNESCO has monitored in a daily basis the status of the schooling system according to the closures of school and the methods selected for delivery across the world since the outbreak of the pandemic

These dashboards present the status of school closures by region and country and the status of the delivery of classes in two dashboards, one by region and the other by country.

Context

The novel coronavirus has taken the world by storm . Italy alone has doubled the no of cases in the last week only . The dataset is aimed to track #no of confirmed cases ,deaths and recovery per day

Content

The data was acquired from github page of John Hopkins university.

Update: As of 23rd March data , recovery cases are not getting updated . Confirmed cases and deaths remain unaffected . Will update on recovery cases in some time

Update: As of 25th March , recovery cases are getting updated . There could possibly some issues as source data is regularly changing as per blog https://github.com/CSSEGISandData/COVID-19/issues/1250

Inspiration

The data will help form interesting insights , trends if any for number of confirmed ,deaths and recovery cases . The data will be updated daily

Archived as of 2/28/22: This dataset will no longer receive updates as of 2/28/2022 due to changing requirements for school reporting. The historical data will continue to be available for download. By school breakdown of counts of infected students, teachers and other infected staff - updated weekly Notes: 11/12/2021: Historical re-infections have been added to the case counts for all pertinent COVID datasets back to 9/1/2021 and new re-infections will be added to the total case counts as they are reported in accordance with CDC guidance. Note 9/13/21 this dataset has been updated to include school years. Please see data dictionary for details. 9/10/21: Due to technical issues, this dataset has not been updated since 8/30, and will be updated as soon as the issue is resolved.

Abstract

COVID-19 caused significant disruption to the global education system. A thorough analysis of recorded learning loss evidence documented since the beginning of the school closures between March 2020 and March 2022 finds even evidence of learning loss. Most studies observed increases in inequality where certain demographics of students experienced more significant learning losses than others. But there are also outliers, countries that managed to limit the amount of loss. This review consolidates all the available evidence and documents the empirical findings. Data for 41 countries is included, together with other variables related to the pandemic experience. This data is publicly available and will be updated regularly.

Geographic coverage

The data covers 41 countries.

Analysis unit

Country

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

COVID-19 has fundamentally altered how education is delivered. Gordon et al. previously conducted a review of medical education developments in response to COVID-19; however, the field has rapidly evolved in the ensuing months. This scoping review aims to map the extent, range and nature of subsequent developments, summarizing the expanding evidence base and identifying areas for future research. The authors followed the five stages of a scoping review outlined by Arskey and O’Malley. Four online databases and MedEdPublish were searched. Two authors independently screened titles, abstracts and full texts. Included articles described developments in medical education deployed in response to COVID-19 and reported outcomes. Data extraction was completed by two authors and synthesized into a variety of maps and charts. One hundred twenty-seven articles were included: 104 were from North America, Asia and Europe; 51 were undergraduate, 41 graduate, 22 continuing medical education, and 13 mixed; 35 were implemented by universities, 75 by academic hospitals, and 17 by organizations or collaborations. The focus of developments included pivoting to online learning (n = 58), simulation (n = 24), assessment (n = 11), well-being (n = 8), telehealth (n = 5), clinical service reconfigurations (n = 4), interviews (n = 4), service provision (n = 2), faculty development (n = 2) and other (n = 9). The most common Kirkpatrick outcome reported was Level 1, however, a number of studies reported 2a or 2b. A few described Levels 3, 4a, 4b or other outcomes (e.g. quality improvement). This scoping review mapped the available literature on developments in medical education in response to COVID-19, summarizing developments and outcomes to serve as a guide for future work. The review highlighted areas of relative strength, as well as several gaps. Numerous articles have been written about remote learning and simulation and these areas are ripe for full systematic reviews. Telehealth, interviews and faculty development were lacking and need urgent attention.

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus.

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

LearnPlatform is a technology platform in the kindergarten-12th grade (K-12) market providing a broadly interoperable platform to the breadth of educational technology (edtech) solutions in the United States K-12 field. A key component of edtech effectiveness is integrated reporting on tool usage and, where applicable, evidence of efficacy. With COVID closures, LearnPlatform is a resource to measure whether students are accessing digital resources within distance learning constraints. This platform provides a source of data to understand if students are accessing digital resources, and where resources have disparate usage and impact. This study includes educational technology usage across over 8,000 tools used in the education field in 2020.

Context

Amidst the COVID-19 outbreak, the world is facing great crisis in every way. The value and things we built as a human race are going through tremendous challenges. It is a very small effort to bring curated data set on Novel Corona Virus to accelerate the forecasting and analytical experiments to cope up with this critical situation. It will help to visualize the country level out break and to keep track on regularly added new incidents.

COVID-19 Country Level Timeseries Dataset

This Dataset contains country wise public domain time series information on COVID-19 outbreak. The Data is sorted alphabetically on Country name and Date of Observation.

Column Descriptions

The data set contains the following columns:
ObservationDate: The date on which the incidents are observed country: Country of the Outbreak Confirmed: Number of confirmed cases till observation date Deaths: Number of death cases till observation date Recovered: Number of recovered cases till observation date New Confirmed: Number of new confirmed cases on observation date New Deaths: Number of New death cases on observation date New Recovered: Number of New recovered cases on observation date latitude: Latitude of the affected country longitude: Longitude of the affected country

Acknowledgements

This data set is a cleaner version of the https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset data set with added geo location information and regularly added incident counts. I would like to thank this great effort by SRK.

Original Data Source

Johns Hopkins University MoBS lab - https://www.mobs-lab.org/2019ncov.html World Health Organization (WHO): https://www.who.int/ DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia. BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/ National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html Macau Government: https://www.ssm.gov.mo/portal/ Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0 US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19 Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

DPH note about change from 7-day to 14-day metrics:

As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWRweekoverview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

Notes:

9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The number of children, youth and adults not attending schools or universities because of COVID-19 is soaring. Governments all around the world have closed educational institutions in an attempt to contain the global pandemic.

According to UNESCO monitoring, over 100 countries have implemented nationwide closures, impacting over half of world’s student population. Several other countries have implemented localized school closures and, should these closures become nationwide, millions of additional learners will experience education disruption.

This data is compiled by the UNESCO and distributed by HDX.

Data on distance learning and supplemental feeding programs were collected from a stratified sample of 600 school districts. School districts were divided into quartiles based on the percentage of students eligible for free/reduced-price lunch, an indicator of family economic status, as reported by the National Center for Education Statistics (https://nces.ed.gov/ccd/). A simple random sample was taken in each stratum, and sample size per stratum was calculated using 95% confidence interval of 50% ± 10%. Data on the availability and method of delivery of both distance learning and supplemental feeding programs were collected from publicly available announcements on school district websites and their official social media pages (Facebook, Twitter). Google searches were performed for news resources when information was not available from online district sources.

As of July 1, 2021, the reporting of COVID-19 outbreaks within selected settings by facility name, number of cases and deaths is no longer required by law, and this dataset will no longer be updated. This dataset includes data reported to VDH on outbreaks that occurred in Higher Education Settings in Virginia. The data included are the name of the facility, locality of the facility, date VDH is notified about the outbreak, status of the outbreak, and the number of associated cases and deaths. This data set was first published on January 15, 2021. This data set was last updated on June 25, 2021.

Context

The dataset contains the list of COVID Fake News/Claims which is shared all over the internet.

Content

1. Headlines: String attribute consisting of the headlines/fact shared.
2. Outcome: It is binary data where 0 means the headline is fake and 1 means that it is true.

Inspiration

In many research portals, there was this common question in which the combined fake news dataset is available or not. This led to the publication of this dataset.

Every day, schools, child care centres and licensed home child care agencies report to the Ministry of Education on children, students and staff that have positive cases of COVID-19. If there is a discrepancy between numbers reported here and those reported publicly by a Public Health Unit, please consider the number reported by the Public Health Unit to be the most up-to-date. Schools and school boards report when a school is closed to the Ministry of Education. Data is current as of 2:00 pm the previous day. This dataset is subject to change. Data is only updated on weekdays excluding provincial holidays Effective June 15, 2022, board and school staff will not be expected to report student/staff absences and closures in the Absence Reporting Tool. The ministry will no longer report absence rates or school/child care closures on Ontario.ca for the remainder of the school year. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. ##Summary of school closures This is a summary of school closures in Ontario. Data includes: * Number of schools closed * Total number of schools * Percentage of schools closed ##School Absenteeism This report provides a summary of schools and school boards that have reported staff and student absences. Data includes: * School board * School * City or Town * Percentage of staff and students who are absent ##Summary of cases in schools This report provides a summary of COVID-19 activity in publicly-funded Ontario schools. Data includes: * School-related cases (total) * School-related student cases * School-related staff cases * Current number of schools with a reported case * Current number of schools closed Note: In some instances the type of cases are not identified due to privacy considerations. ##Schools with active COVID-19 cases This report lists schools and school boards that have active cases of COVID-19. Data includes : * School Board * School * Municipality * Confirmed Student Cases * Confirmed Staff Cases * Total Confirmed Cases ##Cases in school board partners This report lists confirmed active cases of COVID-19 for other school board partners (e.g. bus drivers, authorized health professionals etc.) and will group boards if there is a case that overlaps. Data includes : * School Board(s) * School Municipality * Confirmed cases – other school board partners ##Summary of targeted testing conducted in schools This data includes all tests that have been reported to the Ministry of Education since February 1, 2021. School boards and other testing partners will report to the Ministry every Wednesday based on data from the previous seven days. Data includes : * School boards or regions * Number of schools invited to participate in the last seven days * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified ##Summary of asymptomatic testing at conducted in pharmacies: This is a summary of COVID-19 rapid antigen testing conducted at participating pharmacies in Ontario since March 27, 2021. * Total number of tests conducted in the last seven days * Cumulative number of tests conducted * Number of new cases identified in the last seven days * Cumulative number of cases identified

Provisional counts of deaths in the United States by race and educational attainment. The dataset includes cumulative provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

The United States of America