Context

Ohio COVID 19 Data by School District.

Content

From the website: *"This data reflects new and cumulative COVID-19 cases reported to schools by parents/guardians and staff. Schools are required to report cases to their assigned Local Health Department who then report to the Ohio Department of Health. A report of COVID-19 should not be interpreted as an indicator that a school district or school isn’t following proper procedures—school cases can be a reflection of the overall situation in the broader community. Families and staff should always feel free to ask questions of the school district or school.

For more details on schools and the education sector, please see Sector Specific Operating Requirements: https://coronavirus.ohio.gov/wps/portal/gov/covid-19/responsible-restart-ohio/sector-specific-operating-requirements/sector-specific-operating-requirements

School reporting templates, a list of school districts and their corresponding local health departments, and more can be found on the Education and Sector Specific Guidance page under “Schools”: https://coronavirus.ohio.gov/wps/portal/gov/covid-19/responsible-restart-ohio/sector-specific-operating-requirements/sector-specific-operating-requirements

For more details, please see: https://coronavirus.ohio.gov/wps/portal/gov/covid19/dashboards/Schools-and-Children/schools"*

Acknowledgements

The start of Ohio

Inspiration

Visualize on a map (after joining with school district by location), look for trends, etc

The following guidance is directed to administrators of schools from kindergarten to grade 12 (K-12) and local public health authorities (PHAs) in jurisdictions where these schools exist. The guidance is not prescriptive in nature, rather, it supports administrators and PHA's to consider potential risks and mitigation strategies associated with the resumption of in-school classes during the COVID-19 pandemic.

This guidance supports high quality investigations that will contribute to public health’s collective understanding of COVID-19 transmission in all types of school settings and the utility of mitigation measures implemented. A systematic approach to outbreak response, including an investigation that examines cases, contacts, their interactions and environment, will help to produce higher quality evidence and will support public health officials in making evidence informed policy decisions. The guidelines will provide information applicable to any type of outbreak investigation and will highlight specific considerations for outbreaks occurring within an educational setting including daycares and schools.

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.

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 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.

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).

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.

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/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.

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.

The school year 2020/2021 was started by parents in Poland with concerns about the conditions of teaching in schools during the ongoing COVID-19 pandemic. More than 48 percent of parents do not know what the teaching rules will be, and more than 62 percent said that the conditions of education in schools are not safe enough.

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

Please cite the following paper when using this dataset: N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109 Abstract The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset files contain the raw version that comprises 52,868 Tweet IDs (that correspond to the same number of Tweets) and the cleaned and preprocessed version that contains 46,208 unique Tweet IDs. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. Data Description The dataset comprises 7 .txt files. The raw version of this dataset comprises 6 .txt files (TweetIDs_Corona Virus.txt, TweetIDs_Corona.txt, TweetIDs_Coronavirus.txt, TweetIDs_Covid.txt, TweetIDs_Omicron.txt, and TweetIDs_SARS CoV2.txt) that contain Tweet IDs grouped together based on certain synonyms or terms that were used to refer to online learning and the Omicron variant of COVID-19 in the respective tweets. The cleaned and preprocessed version of this dataset is provided in the .txt file - TweetIDs_Duplicates_Removed.txt. The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (link to download the application: https://github.com/DocNow/hydrator/releases and link to a step-by-step tutorial: https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweetsr) may be used. The list of all the synonyms or terms that were used for the dataset development is as follows: COVID-19: Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus online learning: online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures A description of the dataset files is provided below: TweetIDs_Corona Virus.txt – Contains 321 Tweet IDs correspond to tweets that comprise the keywords – "corona virus" and one or more keywords/terms that refer to online learning TweetIDs_Corona.txt – Contains 1819 Tweet IDs correspond to tweets that comprise the keyword – "corona" or "coronaoutbreak" and one or more keywords/terms that refer to online learning TweetIDs_Coronavirus.txt – Contains 1429 Tweet IDs correspond to tweets that comprise the keywords – "coronavirus" or "coronaviruspandemic" and one or more keywords/terms that refer to online learning TweetIDs_Covid.txt – Contains 41088 Tweet IDs correspond to tweets that comprise the keywords – "COVID" or "COVID19" or "COVID-19" and one or more keywords/terms that refer to online learning TweetIDs_Omicron.txt – Contains 8198 Tweet IDs correspond to tweets that comprise the keywords – "omicron" or "omicron variant" and one or more keywords/terms that refer to online learning TweetIDs_SARS CoV2.txt – Contains 13 Tweet IDs correspond to tweets that comprise the keyword – "SARS-CoV-2" and one or more keywords/terms that refer to online learning TweetIDs_Duplicates_Removed.txt - A collection of 46208 unique Tweet IDs from all the 6 .txt files mentioned above after...

Additional file 1: Table S1. PRISMA item checklist for systematic reviews. Table S2. Deviations from the systematic review protocol. Table S3. Searched websites of key organizations. Table S4. Search strategy. Table S5. Reasons for exclusion of studies from the systematic literature search, after full-text screening. Table S6. Data conversion. Table S7. Criteria for grading evidence according to Grading of Recommendations, Assessment, Development and Evaluations (GRADE). Table S8. Evidence profile for grading evidence according to Grading of Recommendations, Assessment, Development and Evaluations (GRADE). Table S9. Summary of effect estimates. Table S10. Meta-regression for total physical activity with categorical moderators. Table S11. Meta-regression for total physical activity with continuous moderators. Table S12. Meta-regression for moderate-to-vigorous physical activity with categorical moderators. Table S13. Meta-regression for moderate-to-vigorous physical activity with continuous moderators. Table S14. Sensitivity analysis for total physical activity. Table S15. Sensitivity analysis for moderate-to-vigorous physical activity. Table S16. Eggers’ test. Figure S1. PRISMA Flow Chart. Figure S2. Graphical distribution of the studies included. Figure S3. Traffic-light plots of the domain-level judgements for each individual result. Figure S4. Weighted-bar plots of the distribution of risk of bias judgements within each bias domain. Figure S5. Forest plot of changes in total physical activity comparing before and during COVID-19 pandemic, using Physical Activity Questionnaire for Children and Adolescents. Figure S6. Forest plot of changes in total physical activity comparing before and during COVID-19 pandemic, using accelerometer measurements. Figure S7. Forest plot of changes in female and male total physical activity comparing before and during COVID-19 pandemic. Figure S8. Forest plot of changes according to time course in total physical activity comparing before and during COVID-19 pandemic. Figure S9. Forest plot of changes according to a restriction length > 30 days before measurement in total physical activity comparing before and during COVID-19 pandemic. Figure S10. Forest plot of changes according to a restriction length > 60 days before measurement in total physical activity comparing before and during COVID-19 pandemic. Figure S11. Forest plot of changes according to a restriction length > 90 days before measurement in total physical activity comparing before and during COVID-19 pandemic. Figure S12. Forest plot of changes in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S13. Forest plot of changes in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic, using self-reported score measurements. Figure S14. Forest plot of changes in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic, using accelerometer measurements. Figure S15. Forest plot of changes in female and male moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S16. Forest plot of changes in moderate-to-vigorous physical activity comparing different age groups. Figure S17. Forest plot of changes according to time course in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S18. Forest plot of changes according to a restriction length > 30 days before measurement in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S19. Forest plot of changes according to a restriction length > 60 days before measurement in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S20. Forest plot of changes according to a restriction length > 90 days before measurement in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S21. Funnel plot of changes in total physical activity comparing before and during COVID-19 pandemic. Figure S22. Funnel plot of changes in moderate-to-vigorous physical activity comparing before and during COVID-19 pandemic. Figure S23. Funnel plot of changes in sporting activity comparing before and during COVID-19 pandemic.

This resource guide is designed to assist Alberta schools in addressing COVID-19 in the school setting. This includes information on case notification, school follow-up, exclusions, outbreaks and public reporting.

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.

Analysis of ‘CT School Learning Model Indicators by County (14-day metrics) - ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/feda0dbb-905d-48c8-81ec-590689a6da8f on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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).

--- Original source retains full ownership of the source dataset ---

For questions or comments about the database please contact:
Rachel Scheckman
rscheckm@bu.edu
Database Manager Department of Health Law, Policy & Management
Boston University School of Public Health

Database of state policies on closures, shelter-in-place orders, housing protections, changes to Medicaid and SNAP, physical distancing closures, reopening, and more created by researchers at the Boston University School of Public Health. Policies included are state-wide directives or mandates, not guidance or recommendations. In order for a policy to be included, it must apply to the entire state. We are working quickly to go through state government websites to make the policy database as complete and accurate as possible in a rapidly changing policy context. If you use data on a given policy, we encourage you to triangulate based on additional sources of policy data and to review the source documentation to consider the coding decisions that are right for your work. Of course, please email us if you note a discrepancy so we can improve the database for everyone. State policy source documentation can be found at: tinyurl.com/statepolicysources.

This story was originally published in February 2020. While the maps in the story are automatically updated with latest available statistics, the text may include information that is no longer current. For the latest guidelines on coronavirus prevention and mitigation, please visit the CDC's or WHO's information pages.Since December 2019, the novel coronavirus pandemic has touched nearly every country on the planet, and upended the lives of hundreds of millions of people, according to official and unofficial statistics compiled by researchers at Johns Hopkins University.The novel coronavirus belongs to the same family of viruses that cause severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). COVID-19, as the disease is known, produces mild symptoms in most people, but can also lead to severe respiratory illness.

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.

BackgroundA number of public health measures are required during the COVID-19 pandemic. To stop the spread of COVID-19, the Chinese government has adopted isolation policies, including closing non-essential businesses, public transportation and schools, moving students' face-to-face learning to online, and recommending the cancellation of all non-essential activities and outdoor activities. However, while this isolation strategy has reduced human-to-human transmission of COVID-19, it has led to dramatic changes in students' daily lives and learning styles, including reduced physical activity and increased sedentary time. Considering the potentially harmful effects of physical inactivity, this study hoped to explore the incidence and influencing factors of non-participation in home physical exercise among Chinese students aged 10–20 during the implementation of the COVID-19 isolation policy.MethodsThrough an online questionnaire platform, this study created an open-ended questionnaire (from March 1, 2020 to March 10, 2020) and distributed it to students in areas where isolation policies were enforced. The questionnaire was initially distributed by 10 recruited volunteers, and then the questionnaire was voluntarily forwarded and shared by the subjects or others, in a “snowball” way, to expand distribution. Finally, the survey data of 4,532 Chinese students aged 10–20 were collected. The incidence of respondents non-participating in home physical activity was determined using univariate analysis. Using odds ratios and 95% confidence intervals of a multivariate binary logistic regression model, factors influencing non-participation in home physical exercise were estimated.ResultsAmong the sample students, the incidence rate of non-participating in home physical exercise was 25.86% (24.06–27.15%). Exercise intentions, exercise habits, self-assessed health, beliefs in physical health, family exercise, family exercise recommendations, home exercise conditions, school exercise guidance, and health education programs had a negative impact on students non-participating in home physical exercise. Academic performance and electronic product use had a positive effect on non-participating in home physical exercise.ConclusionsA variety of forward leaning factors, enabling factors and demand factors have affected the occurrence of students” non-participating in home physical exercise. Future health isolation policies should take into account these influencing factors to reduce the occurrence of students” non-participating in home physical exercise and to promote students' independent participation in physical exercise.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students