Explore Education Statistics data set Table 1 - Daily attendance in state funded schools during the COVID-19 outbreak From 1 September from Attendance in education and early years settings during the coronavirus (COVID-19) pandemic

The share of K-12 schools in the United States who taught virtually has declined since September 2020. At the beginning of September 2020, **** percent of K-12 schools were teaching students virtually due to the COVID-19 pandemic. This figure declined to **** percent of K-12 schools by February 2021.

The 2021-2022 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2021-2022 school year and the Fall 2022 semester, from August 2021 – December 2022.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

• Data from August 1, 2021 to June 24, 2022 correspond to the 2021-2022 school year. During this time frame, data from the AEI/Return to Learn Tracker and most state dashboards were not available. Inferred modalities with a probability below 0.6 were deemed inconclusive and were omitted. During the Fall 2022 semester, modalities for districts with a school closure reported by Burbio were updated to either “Remote”, if the closure spanned the entire week, or “Hybrid”, if the closure spanned 1-4 days of the week.
• Data from August 1, 2022 to December 31, 2022 correspond to the 2022-2023 school year and were processed in a similar manner to data from the 2021-2022 school year.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

Data Information

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

Technical Notes

• Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

Sources

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

In September 2020, the school year begins in Poland. The government has decided not to impose extraordinary obligations on schools such as wearing masks. As part of the recommendations, schools are obliged to observe hygiene, airing the rooms, or changing the classes' organization. Every second Polish respondent assessed this decision negatively. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In September 2020, the academic year begins in Poland. As of late August 2020, a total share of 59 percent of Polish fathers was concerned that their children might get infected with coronavirus or the flu when attending school in the new school year.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This file contains weekly attendance data at local authority level for state-funded education settings for each Thursday in the autumn term (from 10 September until 17 December 2020) and for the spring term (13 January until 1 April 2021). It also includes workforce absence statistics in the autumn term (from 10 September until 17 December 2020) and the spring term (13 January until 1 April 2021).The data is shown for each local authority and is further split by the following school phases:state-funded secondary schoolsstate-funded primary schoolsstate-funded special schoolsall state-funded schools.Data is in this file has been not been scaled to account for non-response so it is not nationally representative.

This dataset includes information on school reported COVID-19 testing and case positive data beginning on September 13, 2021. Data is collected from charter schools on each operational day using the daily school survey form, which school administrators access by logging in to the NYSDOH school survey website. Each school may submit data for the current operational day between the hours of 7am and 5pm. At 5pm, a process runs which aggregates data at the school and district levels. The following morning the data are updated for the previous day and displayed on the School COVID Report Card 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.

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.

This file contains levels of workforce absence rates in education settings from September 2021. Data is in this file has been scaled to account for non-response so it is nationally representative.

At the beginning of 2020 national school food programs reached more children than any time in history making school food programs the most extensive form of social safety nets in the world. Looking to Canada, school food programs across the country serve more than 1 million students and provide multifaceted benefits including access to healthy fresh food choices, improving learning capacities, promoting nutritional awareness, assisting food-insecure households, and promoting local food procurement. However, since the beginning of the SARS-Cov 2 (COVID-19) pandemic these programs have faced operational challenges resulting in many rolling back their operations while food insecurity rates in Canada have increased dramatically. Framed as a Canadian case study analysis, this paper considers the discursive effects of provincial and territorial school reopening plans and the material consequences felt by SFPs. Specifically, this paper considers the reach, effectiveness, adoption, implementation, and maintenance of provincial and territorial school food programs within the broader conceptualization of ecological public health to consider if these programs were enabled or constrained by school reopening plans. The authors conducted a policy analysis of 57 primary and 164 supportive school reopening documents developed between April 2020 and September 2021. It was found that provincial and territorial school reopening plans primarily focused on measures to limit infectious disease transmission while food discussed in broad terms demonstrated policy makers' limited awareness of the important role of school food programs and support required to maintain them. In turn, two key observations were made: 1) government school reopening plans have overlooked the benefits of school food programs in Canada, and 2) school reopening plan designers missed opportunities to improve school food programs. This paper argues a thorough understanding of the impacts to school food programs by provincial and territorial COVID-19 public health guidelines is needed for politicians, policymakers, and school food practitioners to support the short- and long-term capacity of these programs and to ensure food insecurity and nutritional health issues in Canada continue to be on thepolitical agenda.

We survey 4162 parents across five rural districts in Jharkhand—Bokaro, Dhanbad, Purbi Singhbhum, Ramgarh, and Ranchi—and evaluate the learning levels of their wards. Through these surveys, we aim to examine three research questions: i) What are the different types of service providers in the K-12 sector in Jharkhand and how, as per the parents surveyed, did these schools pivot during the pandemic? (ii) How did parents perceive the pivoted services availed by their children? (iii) What were the students' learning outcome levels post-COVID? The data was collected between August and September 2022. Note: personally identifiable data such as name, mobile number, and geo-coordinates have been removed to maintain anonymity. A separate code-book has been attached with the full data-set.

Ethics Reference: EFEC 2-04/2023The perceptions of teachers on how effective small classes and rotational timetables were in curriculum recovery during the COVID-19 pandemic in South African schools.Qualitative data was collected. Data was collected using a Google Forms survey, which contained 12 open-ended questions about the teachers’ experiences of using small classes and rotational timetables during the lockdown, and how these changes affected curriculum recovery during the COVID-19 pandemic. Google Forms were chosen to collect data as it gave participants freedom to complete the questionnaire anywhere and the feedback was sent to the researcher as soon as the questionnaire was completed. The researcher also intended to conduct interviews after the questionnaires were completed, using the same questions from the questionnaire, to give the participants space to ellaborate. The researcher had permission from the WCED to approach schools between 1 August 2023 and 30 September 2023 and during this time he set up meetings with the principals to explain the study and gave the participants two weeks to complete the questionnaire. Using qualitative research in this study helped design questions that valued the participants’ lived experience. The results of this study have indicated that the use of small classes and rotational timetables somehow helped the participants to recover lost teaching and learning time, albeit with challenges, most of which were associated with the employment of rotational timetables. The analytical discussion presented above situates the ineffectiveness of these strategies at a deeper level than just participants and learners, but rather in the echelons of family, school, WCED and the DBE. These are the structures that should have provided participants with the support they needed to recover the curriculum effectively. Parents should have supported the learners and ensured that they attended school regularly on the days they were scheduled to attend, and that they did their homework promptly, thereby strengthening the efforts of recovering the academic curriculum. In addition, the schools, the WCED and the DBE should have had contingency plans in place for the instability brought about by the COVID-19 pandemic. In other words, they should have been more proactive and responsive. These plans should have directly addressed how the schools that could not continue with online teaching and learning had to function so that no learner was left behind. It is clear that without the interventions of these systems, teachers were bound to encounter challenges along the way.

The data in these spreadsheets represent an export of summary and raw learning model data provided by Local Education Authorities (LEAs - typically school districts and charter schools), for the 2020-2021 school year. This collection began in September 2020, for learning models that implement the Minnesota Safe Learning Plan, and ended for almost all formal purposes on July 1, 2021. This data serves as an archive of the plans provided by LEAs to the MN Department of Education.

For more information about the department's efforts to support schools during the COVID-19 pandemic, visit https://education.mn.gov/MDE/dse/health/covid19/

For more information about the specific CSV files included, see the specific metadata records at:
Safe Learning Models (raw data)
Learning Models District Level (summary data)

The COVID-19 pandemic brought many disruptions to children’s education, including the education of children with intellectual (learning) disability and/or autism. We investigated the educational experiences of autistic children and children with an intellectual disability about a year after the COVID-19 pandemic started in the UK.

An online survey collected data during the summer/autumn of 2021 from 1,234 parents of 5 to 15 year-old children across all 4 UK countries. The study investigated school attendance and home learning experiences of children with intellectual disability and/or autistic children who were registered to attend school in 2021. The study also investigated the experience of Elective Home Education in families of children with a neurodevelopmental condition whose child was de-registered from school before and after the pandemic started in the UK in March 2020.

The study provided evidence on the impact of COVID-19 on school attendance and home education for children with a neurodevelopmental condition.Education changed dramatically due to the COVID-19 pandemic. Schools closed in 2019/20. There was compulsory return to school in September 2020 with measures in place to control infection and new regulations about COVID-19-related absences. School attendance in the first term of 2020-21 was lower compared to other years. Many children were de-registered from school. In early 2020-21, there was a second prolonged period of national school closures. The pandemic has caused many disruptions to children's education.

Children with neurodevelopmental conditions (NDCs), in particular intellectual disability and autism, are the most vulnerable of vulnerable groups. Among children with special educational needs and disabilities (SEND), children with intellectual disability and/or autism consistently struggle to meet the required standards in education. Our study will focus on these two groups of children.

Before the pandemic, many children with NDCs missed school. Then the pandemic disrupted everyone's education. Approximately one year after the pandemic started, we will investigate the educational experiences of children with NDCs.

Our project will investigate: - School absence and reasons for absence among children with intellectual disability and/or autism - Child, family, and school factors associated with school absence - Barriers and facilitators of school attendance - Parents' experiences of home schooling

An online survey will collect data from approximately 1,500 parents of 5 to 17 year-old children with NDCs across all 4 UK countries. We will recruit parents of: (i) children registered with a school in spring/summer 2021; (ii) children not registered with a school in spring/summer 2021 but who were registered with a school at the start of the pandemic in March 2020; and (iii) children not registered with a school on either date. We will collect data on school attendance for those registered with a school, and data on home learning experiences for those not registered with a school. For all children, we will collect data on their mental health.

The first analysis will investigate school absence with a focus on children registered with a school. We will summarise school absence data as well as reasons for absence as reported by the parents. The second analysis will investigate school attendance: attending school or home schooling. We will describe the children currently registered to attend school (group 1), those not currently registered who were registered in March 2020 at the start of the pandemic (group 2), and those not registered on either point (group 3). We will summarise the reasons parents give for de-registering their child from school. Our final analysis will focus on home learning support during home schooling. We will describe the types of support schools offer to school-registered students during remote learning (when students are self-isolating/shielding, or schools are closed because of lockdown). We will describe the home learning experiences of school de-registered children and parents' satisfaction with these arrangements.

We will work closely with parents of children with NDCs, seeking their advice on the study. Our team includes the Council for Disabled Children, the largest umbrella organization in the UK bringing together many charities supporting disabled children and their families. We will share the study findings widely, including key messages for policies related to the education of children with special educational needs and disabilities.

Using a sample of 1,211 households in Pakistan, we examine the effects of COVID-19 on three key domains: education, economic, and health-related. First, during school closures, 66 percent of surveyed households report not using technology for learning at all. Wealth disparities mar access to distance learning, and richer households are 39 percent more likely to use technology for learning compared to the poorest households. This has implications for learning remediation as children head back to school. Second, more than half of the respondents report a reduction in income and one-fifth report being food insecure during the lockdown in the first week of May, 2020. Only one-fifth of households reporting a reduction in income and one-fifth of respondents reporting a reduction in the number of meals consumed report being covered by the federal government’s cash transfer program. Third, while a majority of respondents (90 percent) report adopting precautionary measures such as face masks, a vast majority of respondents (78 percent) underestimate the risk of contracting a COVID-19 infection compared to tuberculosis. With schools reopening in a phased manner since mid-September, most respondents (68 percent) believe that school reopenings will further increase the risk of COVID-19 infections. (2020)

Parents that selected one of the below factors as the top 5 priorities for reopening schools and resuming in-person classes.

BackgroundChildhood obesity and pediatric metabolic syndrome (MetS) have steadily increased during the last decade in Saudi Arabia. Intervention programs to prevent cardiometabolic disorders in Arab youth are needed.ObjectiveIn this multi-school intervention study which was disrupted by COVID-19-imposed lockdowns (September 2019–April 2021), a 12-month lifestyle education program focused on improving the cardiometabolic status of Arab adolescents was investigated.MethodsA total of 2,677 Saudi students aged 12–18 years were recruited from 60 different secondary and preparatory year schools in Riyadh city, Saudi Arabia. The intervention was initially in-person counseling sessions and the subsequent sessions conducted virtually post-pandemic. Baseline anthropometrics and fasting blood samples for glucose, HbA1c, and lipid assessments were collected at baseline and after 12 months (704 participants).ResultsOnly 704 out of 2,677 (73.7% dropout) completed the intervention. At baseline, 19.6% of the participants were overweight and 18.1% were obese. A modest but significant decrease in the prevalence of central obesity [11.2 vs. 6.7% (−4.5% change, p = 0.002)], hypertension [22.3 vs. 11.4% (−10.9% change, p < 0.001)], and low-HDL cholesterol [61.6 vs. 23.3% (−38.3% change, p < 0.001)] was noted. Consequently, the prevalence of hypertriglyceridemia increased from 22.7 to 56.3% (+ 33.6%, p < 0.001) overtime. Also, the proportion of subjects who were able to change their status from MetS to non-MetS was significantly more in overweight/obese at baseline than normal weight (16.9 vs. 3.6%, adjusted OR = 3.42, p < 0.001).ConclusionInterrupted lifestyle education programs secondary to COVID-19-imposed lockdowns still provided modest effects in improving cardiometabolic indices of Arab adolescents. Given the high digital literacy of Arab youth, improving the delivery of virtual lifestyle education programs may prove beneficial.

This archive contains the final data freeze for COVID Schools Canada, and the software used to compile, clean, and plot the data.

The Canada COVID-19 School Tracker was a 100% volunteer-led project tracking COVID-19 cases and outbreaks in schools across Canada from September 2020 to June 2021. The goal of this project was to highlight the impact of COVID-19 on schools and families; to advocate for safer schools; and to advocate for transparency in our educational system. This project is an initiative of grassroots advocacy group Masks4Canada.

To learn more about project and see the interactive map of COVID-19 cases and outbreaks across Canadian schools as compiled by this project, visit https://covidschoolscanada.org/

For questions about these data, please contact Shraddha Pai.

During the COVID-19 pandemic, many public schools across the United States shifted from fully in-person learning to alternative learning modalities such as hybrid and fully remote learning. In this study, data from 14,688 unique school districts from August 2020 to June 2021 were collected to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. These data were provided by Burbio, MCH Strategic Data, the American Enterprise Institute’s Return to Learn Tracker and individual state dashboards. Because the modalities reported by these sources were incomplete and occasionally misaligned, a model was needed to combine and deconflict these data to provide a more comprehensive description of modalities nationwide. A hidden Markov model (HMM) was used to infer the most likely learning modality for each district on a weekly basis. This method yielded higher spatiotemporal coverage than any individual data source and higher agreement with three of the four data sources than any other single source. The model output revealed that the percentage of districts offering fully in-person learning rose from 40.3% in September 2020 to 54.7% in June of 2021 with increases across 45 states and in both urban and rural districts. This type of probabilistic model can serve as a tool for fusion of incomplete and contradictory data sources in order to obtain more reliable data in support of public health surveillance and research efforts.