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

This project investigated parent perceptions of COVID19 Schooling from home based on a national survey of parents. Survey questions are listed below:• What is your usual employment?• How many hours a week are you currently employed?• What is your age?• What is your gender?• Country of residence• State• Postcode• How many children are currently under your care?• How many children are you currently schooling at home?• What is your child’s age?• What year of school is your child in?• What is your child’s gender?• Does your child have any special learning needs, and if so, what are they?• What type of school does your child attend?• In what area is your child’s school located?• What sort of technology or device does your child most often use for schooling at home (e.g. iPad, Chromebook, ACER laptop, Samsung phone, none)?• Which would best describe the access that your child has to a device or technology in order to undertake schooling at home?• Approximately how many weeks in total have you schooled your child from home since the beginning of the COVID-19 pandemic?• Approximately how many hours a week do you personally support your child to undertake schooling at home?• Approximately how many hours a week does another adult or adults support your child to undertake schooling at home?• Please rate your agreement with the following questions:- Schooling at home has been stressful for me.- Schooling at home has been difficult for my child.• What has been most stressful and difficult for you and your child about homeschooling, and why?• What has worked well/has been beneficial for you or your child during homeschooling, and why?• How many days each week does your child undertake schooling at home?• On each schooling at home day, approximately how many hours does your child spend schooling at home?• Are you generally aware of how your child spends their time completing schooling at home?• Approximately how many minutes each day (on average) would you estimate your child spends completing each of the following schooling-related activities?- Paper based activities (e.g. printed worksheets)- Offline tactile activities (e.g., exercise, science experiments)- Web-conferencing with a teacher (e.g. via Zoom)- Online learning games (e.g. Mathletics, Reading Eggs)- Digital worksheets completed online (e.g. fill-in-the-blank)- Reading online resources (e.g. links to websites)- Watching videos (teacher created)- Watching videos (general public domain)- Digital creativity tasks (e.g. creating essays, videos, posters)- Other online tasks (e.g. Google Classroom, Moodle chats)- Other:• If you could change anything about your child’s online and offline schooling at home activities, what would it be?• Does your child learn more, the same or less when schooling from home compared to when learning at school?• How much more or less do you estimate your child is learning during schooling at home compared to their normal learning when at school?• Please rate your agreement with the following questions:- My child is able to learn independently using technology- I am satisfied with the homeschooling support being offered by my child’s school• Compared to the first time during the pandemic that you had to do schooling at home, how would you rate schooling at home now?• Please explain the reasons for your answer to the previous question.

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, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

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 a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

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/MMWR_week_overview.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.

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.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

ALL FILES ARE LOCATED AT MY REPOSITORY: https://github.com/christianio123/TexasAttendance

Context

I was curious about factors affecting school attendance so I gathered data from school districts around Texas to have a better idea.

Topic/Purpose

The purpose of the project is to help determine factors associated with student attendance in the state of Texas. No population is targeted as an audience for the project, however, anyone associated in education may find the dataset used (and other data attained but not used) helpful in any questions they may have regarding student attendance in Texas for the first two months of the 2020-2021 academic school year. This topic was targeted specifically due to the abnormalities in the current academic school year.

Data

Daily Student Attendance

Majority of the data in this project was collected by school districts around the state of Texas, public census information, and public COVID 19 data. To attain student attendance information, an email was sent out to 40 school districts around the state of Texas on November 2nd, 2020 using the Freedom of Information Act (FOIA). Of those districts, 19 responded with the requested data, while other districts required purchase of the data due to the number of hours associated with labor. Due to ambiguity in the original message sent to districts, varying types of data were collected. The major difference between the data received was the “daily” records of student attendance and a “summary” of student attendance records so far, this academic school year. School districts took between 10 to 15 business days to respond, not including the holidays. The focus of this project is “daily student attendance” in order to find relationships or any influences from external or internal factors on any given school day. Therefore, of the 19 school districts that responded, 11 sent the appropriate data.
The 11 school districts that sent data were (1) Conroe ISD, (2) Cypress-Fairbanks ISD, (3) Floydada ISD, (4) Fort Worth ISD, (5) Pasadena ISD, (6) Snook ISD, (7) Socorro ISD, (8) Klein ISD, (9) Garland ISD, (10) Dallas ISD, and (11) Katy ISD. However, even within these datasets, there were discrepancies, that is, three school districts sent daily attendance data including student grade level but one school district did not include any other information. Also, of the 11 school districts, nine school districts included student attendance broken down by school while three other school districts only had student attendance with no other attributes. This information is important to explain certain steps in analysis preparation later. Variables used from school district datasets included (a) dates, (b) weekdays, (c) school name, (d) school type, (e) district, and (f) grade level.

School Information, County Description, Metropolitan vs. Non-Metropolitan

  In addition to daily student attendance data, two other datasets were used from the Texas Education Agency with data about each school and school district. In one dataset, “Current Schools”, information about each school in the state of Texas was given such as address, principal, county name, district number and much more as of May 2020. From this dataset, variables selected include (a) school name, (b) school zip, (3) district number, (4) and school type. In the second dataset, “District Type”, attributes of each school district were given such as whether the school district was considered major urban, independent town, or a rural area. From “District Type” dataset, selected variables used were (a) district, district number, Texas Education Agency (TEA) description, and National Center of Education Statistics (NCES). To determine if a county is metropolitan or non-metropolitan, a dataset from the Texas Health and Human Services was used. Selected variables from this dataset include (a) county name and (b) metro area. 

Other Factors: COVID-19

 Student attendance has been noticeably different this academic school year, therefore live COVID-19 data was attained from the New York Times to examine for any relationship. This dataset is updated daily with data being available in three formats (country, state, and county). From this dataset, variables selected were both COVID-19 cases by state, and by county.

Other Factors: Demographics

Each school has a unique student population, therefore census data from 2018 (with best estimate of today’s current population) was used to find the makeup of the population surrounding a school by zip code. From the census data, variables selected were zip code, race/ethnicity, medium income, unemployment rate, and education. These variables were selected to determine differences between school attendance based on the makeup of the population surrounding the school.

Other Factors: Weather

  Weather seems to have an impact on student attendance at schools, so weather data has been included based on county measures.

Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

This dataset shows the attendance rates for all NSW government schools in Semester One by alphabetical order. \r \r Data Notes:\r \r * 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.\r \r * 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.\r \r * In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.\r \r * Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.\r \r * The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.\r \r * Data is suppressed "sp" for schools where student numbers are below the reporting threshold.\r \r * Data is not available "na" for senior secondary schools or other schools where no students were enrolled in Years 1-10.\r \r * Blank cells indicate no students were enrolled at the school that census year or the school was out of scope for attendance reporting.\r \r \r Data Source:\r \r * Education Statistics & Measurement, Centre for Education Statistics and Evaluation

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused the Coronavirus Disease 2019 (COVID-19) worldwide pandemic in 2020. In response, most countries in the world implemented lockdowns, restricting their population's movements, work, education, gatherings, and general activities in attempt to “flatten the curve” of COVID-19 cases. The public health goal of lockdowns was to save the population from COVID-19 cases and deaths, and to prevent overwhelming health care systems with COVID-19 patients. In this narrative review I explain why I changed my mind about supporting lockdowns. The initial modeling predictions induced fear and crowd-effects (i.e., groupthink). Over time, important information emerged relevant to the modeling, including the lower infection fatality rate (median 0.23%), clarification of high-risk groups (specifically, those 70 years of age and older), lower herd immunity thresholds (likely 20–40% population immunity), and the difficult exit strategies. In addition, information emerged on significant collateral damage due to the response to the pandemic, adversely affecting many millions of people with poverty, food insecurity, loneliness, unemployment, school closures, and interrupted healthcare. Raw numbers of COVID-19 cases and deaths were difficult to interpret, and may be tempered by information placing the number of COVID-19 deaths in proper context and perspective relative to background rates. Considering this information, a cost-benefit analysis of the response to COVID-19 finds that lockdowns are far more harmful to public health (at least 5–10 times so in terms of wellbeing years) than COVID-19 can be. Controversies and objections about the main points made are considered and addressed. Progress in the response to COVID-19 depends on considering the trade-offs discussed here that determine the wellbeing of populations. I close with some suggestions for moving forward, including focused protection of those truly at high risk, opening of schools, and building back better with a economy.

This data set shows the average attendance rate for students in NSW government schools by Statistical Area 4 (SA4).\r \r

Data notes\r

• 2021 data is not comparable to previous years due to the continued effects of the COVID-19 pandemic, changes to calculation rules to align with ACARA’s national standards (version 3) and changes to the way attendance data is transferred into the department’s centralised data warehouse. Please refer to 2021 Semester 1 student attendance factsheet for more information.\r \r
• 2020 data is not provided because students were encouraged to learn from home for several weeks in Semester 1. Please refer to the factsheet on The effects of COVID-19 on attendance during Semester 1 2020 for more information.\r \r
• In 2018 NSW government schools implemented the national standards for student attendance data reporting. This resulted in a fall in attendance rates for most schools due to the inclusion of part day absences and accounting for student mobility in the calculation. Data from 2018 onwards is not comparable with earlier years.\r \r
• Schools for Specific Purposes (SSPs) are only included from 2021. Prior to this SSP attendance data was not collected centrally.\r \r
• The attendance rate is defined as the number of actual full-time equivalent student days attended by full-time students in Years 1–10 as a percentage of the total number of possible student-days attended in Semester 1. Figures are aligned with the National Report on Schooling and the My School website.\r \r
• SA4 refers to the ABS Australian Statistical Geography Standard (ASGS) Edition 3 Statistical Area 4 (SA4) – 2021.\r \r
• ‘Other Territories’ has been assigned to Norfolk Island Central School, which operated under the responsibility of NSW Department of Education between 2018-2021.\r \r ### Data source\r Semester 1 Return of Absences Collection\r \r ### Data quality statement\r The Attendance Data Quality Statement addresses the quality of the Attendance dataset using the dimensions outlined in the NSW Department of Education's data quality management framework: institutional environment, relevance, timeliness, accuracy, coherence, interpretability and accessibility. It provides an overview of the dataset's quality and highlights any known data quality issues.\r

Information about school immunization requirements and data

Around 2.17 million pupils were eligible for free school meals in England in the 2024/25 academic year, compared with 2.09 million pupils in the previous year. Free school meals became a key issue during the COVID-19 pandemic of 2020, when they were replaced by a voucher scheme in the lockdown and Easter holidays. Although the voucher system was initially not supposed to extend to the summer holidays, a pressure campaign by English footballer, Marcus Rashford resulted in a government U-turn, on the issue, resulting in the voucher scheme covering the summer.

In Summer 2025, GCSE students in the United Kingdom had a pass rate (achieving a grade of C/4 or higher) of 67.4 percent, compared with 67.6 percent in the previous year. The COVID-19 pandemic, and closure of schools in the UK led to exams throughout the country being cancelled, with grades in 2020 and 2021 based on assessment by teachers and schools. During this provided time period, the highest pass rate was reported in 2021, when 77.1 percent of GCSE entries achieved a pass grade, while it was lowest in 1988, when just 41.9 percent of entries were awarded a pass grade. Gender attainment gap Among female students, the proportion of GCSE entries that received a pass rate in 2025 was 70.5 percent, compared with 64.3 percent of male students. This attainment gap between male and female students has been a consistent feature of GCSE exam results in recent years, with female A-Level students also outperforming their male counterparts. Among undergraduates, this gap is less pronounced, with UK degree results for 2023/24 showing female undergraduates attaining only slightly higher grades than males. Growing negativity about UK education system According to a survey conducted in April 2025, approximately 39 percent of British adults thought that education across the country was in a bad shape, compared with 31 percent who thought it was doing well. This is down from 2021 when just under half of adults believed that the national education system was good, and just 27 percent who thought it was bad. Although education currently lies behind several other issues for Britons in terms of importance, such as the economy, immigration, and health, the growing discontent about education will likely be one of the many issues the current Labour government will have to face in the coming months.

In 2025, 9.4 percent of students in the United Kingdom achieved the highest possible grade (an A*) in their A-Levels, with more than a quarter of entries achieving a B, the most common individual grade level in this year. Grades between 2020 and 2022 were generally a lot higher than in previous years due to the different grading circumstances brought on by the COVID-19 pandemic. Further Education in the UK A-Levels are the main academic qualifications taken following compulsory education in the UK. Among 16 to 17-year-old's around 43 percent were undertaking A/AS Levels in 2024, making it the most common pathway for this age group after high school. A further 20 percent were studying for other Level 3 qualifications, such as on more vocational BTEC courses, and around 3.5 percent were on apprenticeships, or taking part in work-based learning programs. Approximately 6.2 percent of this age group were not in education, employment, or training (NEET), although the share of this age group in this category has fallen since the mid 2000s. Maths remains most popular subject In Summer 2025, over 112,000 of 882,500 A-Level entries were in Mathematics, making it the most popular subject for students at this level. Maths was followed by Psychology, at around 75,900 entries, Biology at 71,400 entries, and Chemistry at 63,500 entries. The most popular humanities subject was History at 44,700 entries, with English Literature being the most popular English subject that year at 37,900 entries. For the A-Levels more technical equivalent (T-Levels) the most popular subject was that of Education and Early Years, a subject focused on the teaching of young children.