Number of COVID-19 cases (PCR test) in schools by county A graphical representation of the numbers is available on the website Covid19 Location Picture Schools. Character separator is comma, character set is UTF-8

This dataset includes information on school reported COVID-19 testing and case positive data from the 2021-2022 academic year. Data was collected from private schools on each operational day using the daily school survey form, which school administrators access by logging in to the NYSDOH school survey website.

The primary goal of publishing this dataset is to provide users timely information about disease spread and reporting of positive cases within schools. The data will be updated daily, reflecting data submitted by school administrators the previous day.

Initial estimates of staff and pupils testing positive for the coronavirus (COVID-19) across a sample of schools within selected local authority areas in England.

On March 2, 2022 DC Health announced the District’s new COVID-19 Community Level key metrics and reporting. COVID-19 cases are now reported on a weekly basis. District of Columbia Public Schools testing for the number of positive tests and quarantined. Due to rapidly changing nature of COVID-19, data for March 2020 is limited.General Guidelines for Interpreting Disease Surveillance DataDuring a disease outbreak, the health department will collect, process, and analyze large amounts of information to understand and respond to the health impacts of the disease and its transmission in the community. The sources of disease surveillance information include contact tracing, medical record review, and laboratory information, and are considered protected health information. When interpreting the results of these analyses, it is important to keep in mind that the disease surveillance system may not capture the full picture of the outbreak, and that previously reported data may change over time as it undergoes data quality review or as additional information is added. These analyses, especially within populations with small samples, may be subject to large amounts of variation from day to day. Despite these limitations, data from disease surveillance is a valuable source of information to understand how to stop the spread of COVID19.

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 dataset includes information on school reported COVID-19 testing and case positive data from the 2021-2022 academic year. Data was collected from BOCES programs on each operational day using the daily school survey form, which school administrators access by logging in to the NYSDOH school survey website.

The primary goal of publishing this dataset is to provide users timely information about disease spread and reporting of positive cases within schools. The data will be updated daily, reflecting data submitted by school administrators the previous day.

This dataset contains a list of school locations across New York City offering COVID-19 testing locations. The list is sorted by district, school borough and three digit school number.

Characteristics of school-based persons with index cases of COVID-19 and close contacts from 103 K–12 schools, Greene and St. Louis Counties, Missouri, January 25–March 21, 2021.

A substantial fraction of k-12 schools in the United States closed their in-person operations during the COVID-19 pandemic. These closures may have altered the labor supply decisions of parents of affected children due to a need to be at home with children during the school day. In this paper, we examine the impact of school closures on parental labor market outcomes. We test whether COVID-19 school closures have a disproportionate impact on parents of school-age children (ages 5-17 years old). Our results show that both women’s and men’s work lives were affected by school closures, with both groups seeing a reduction in work hours and the likelihood of working full-time but only women being less likely to work at all. We also find that closures had a corresponding negative effect on the earnings of parents of school-aged children. These effects are concentrated among parents without a college degree and parents working in occupations that do not lend themselves to telework, suggesting that such individuals had a more difficult time adjusting their work lives to school closures.

1) Register is as of reporting date 2) Only includes schools and programs in Districts 1-32 and District 75 3) NYCEECs and District PreK Centers are excluded 4) District 75 Home and Hospital Instruction programs and students are excluded 5) Percents are of active students ages 5 and up, not of all students (any four year olds are exluded as they are not yet eligible for vaccination) 1) Enrollment as of last day of reporting period 2) Only schools and programs in Districts 1-32 and District 75 3) NYCEECs and District PreK Centers are excluded 4) District 75 Home and Hospital Instruction programs and students are excluded 5) For consent and consent withdrawal, only Covid-19 testing eligible students are included (Grades 1-12) 6) For unvaccinated population, only students aged 5 or above as of the day before the beginning of the reporting period are included "7) Under the Family Educational Rights and Privacy Act (FERPA), educational agencies and institutions reporting or releasing data derived from education records are responsible for protecting personally identifiable information (PII) in their reports from disclosure. a) If a cell is ≤ 5 the value is suppressed (""S""), and the next highest value in that row is also suppressed (""S""). b) If a cell is within 5 of the total number of students for the subgroup, the value is suppressed (""T""), and the next highest value in that row is also suppressed (""T""). This is necessary, because it is a FERPA violation to disclose that no students in a subgroup were vaccinated. This report includes counts of unvaccinated students, therefore data suppression is necessary on the maximum values also." 8) An empty cell indicates that there are no students for that grade or subgroup

Full edition for scientific use. The survey aimed at collecting representative data of Austrian schools. A stratified sampling approach was used. Parents were asked to state their personal attitudes towards the self-administred coronavirus testing scheme used in Austrian schools, as well as describe the use of the testing scheme by their children.

Estimates from the Schools Infection Survey of pupils testing positive for SARS-CoV-2 antibodies. Including breakdowns by age, sex and region where possible.

The disparate impact of COVID-19 on student learning gains across students’ household income and parental education levels–no additional controls.

The SARS-CoV-2 epidemic continues to have major impacts on children's education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.

BackgroundCOVID-19 testing is critical for identifying cases to prevent transmission. COVID-19 self-testing has the potential to increase diagnostic testing capacity and to expand access to hard-to-reach areas in low-and-middle-income countries. We investigated the feasibility and acceptability of COVID-19 self-sampling and self-testing using SARS-CoV-2 Antigen-Rapid Diagnostic Tests (Ag-RDTs).MethodsFrom July 2021 to February 2022, we conducted a mixed-methods cross-sectional study examining self-sampling and self-testing using Standard Q and Panbio COVID-19 Ag Rapid Test Device in Urban and rural Blantyre, Malawi. Health care workers and adults (18y+) in the general population were non-randomly sampled.ResultsOverall, 1,330 participants were enrolled of whom 674 (56.0%) were female and 656 (54.0%) were male with 664 for self-sampling and 666 for self-testing. Mean age was 30.7y (standard deviation [SD] 9.6). Self-sampling usability threshold for Standard Q was 273/333 (82.0%: 95% CI 77.4% to 86.0%) and 261/331 (78.8%: 95% CI 74.1% to 83.1%) for Panbio. Self-testing threshold was 276/335 (82.4%: 95% CI 77.9% to 86.3%) and 300/332 (90.4%: 95% CI 86.7% to 93.3%) for Standard Q and Panbio, respectively. Agreement between self-sample results and professional test results was 325/325 (100%) and 322/322 (100%) for Standard Q and Panbio, respectively. For self-testing, agreement was 332/333 (99.7%: 95% CI 98.3 to 100%) for Standard Q and 330/330 (100%: 95% CI 99.8 to 100%) for Panbio. Odds of achieving self-sampling threshold increased if the participant was recruited from an urban site (odds ratio [OR] 2.15 95% CI 1.44 to 3.23, P < .01. Compared to participants with primary school education those with secondary and tertiary achieved higher self-testing threshold OR 1.88 (95% CI 1.17 to 3.01), P = .01 and 4.05 (95% CI 1.20 to13.63), P = .02, respectively.ConclusionsOne of the first studies to demonstrate high feasibility and acceptability of self-testing using SARS-CoV-2 Ag-RDTs among general and health-care worker populations in low- and middle-income countries potentially supporting large scale-up. Further research is warranted to provide optimal delivery strategies of self-testing.

As of April 2020, 80 percent of Hungarians agreed with the government's decision to organize written high school graduation exams under strict security regulations. At the same time, 18 percent of respondents believed that the exams should not take place during the coronavirus (COVID-19) outbreak.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

National and international tests have yielded low reading comprehension results for education in Panama, although there is limited information regarding literacy development and performance. There are wide gaps in social inequality, access to technology, and public versus private school achievement. Considering this, after a year off from regular face-to-face classes and a partial transition to online education due to the COVID-19 pandemic, the present study utilizes existing data to carry out a pre-post comparison of the reading performance of fourth (n = 167) and sixth (n = 164) grade students in the province of Panama employing a subsample stratified by educational system for comparability (Mann–Whitney U test, α = 0.05). The pre-post comparison was also carried out independently in both the public (n = 235) and private (n = 106) systems, as well as an additional comparison of the average weekly hours of online academic engagement in both systems during the pandemic in fourth (n = 117) and sixth grade (n = 109). The results support a significant decrease in reading performance. Based on the comparative analysis, findings indicate that public school students interacted online with their teachers significantly less than their private schools’ counterparts; and that, in the same sample, only the public-school students exhibited a significant decrease in reading speed by phonological and lexical route with a medium effect size compared to pre-pandemic standards, greater than those reported in other contexts. This highlights the need to develop effective strategies to narrow the existing educational gaps in the country, which seem to have widened due to the pandemic, with particular emphasis on reading performance in primary school.

Percentage of pupils in year 3 who participated in all tests who passed all tests for the subject test in Swedish and Swedish as a second language, municipal schools, (%). Due to Covid-19, no tests were carried out for year 3 in 2020 or 2021, so no data are available for the years. Data is available according to gender breakdown.