2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

The objective of the U.S. COVID-19 County Policy (UCCP) Database was to systematically gather, characterize, and assess geographic and longitudinal variation in U.S. COVID-19-related policies at the county and state levels. The research team gathered policy data on a weekly basis for 309 counties in 50 states and Washington D.C. Although these counties were not nationally representative, they included over half of the U.S. population and were diverse with respect to geography, the race/ethnicity of residents, and political climate. Weekly data were collected between January 2020 and December 2021 on a wide range of COVID-19-related policies that were in effect, providing a longitudinal picture of county policies during that period.

This data was sourced from the Johns Hopkins University Coronavirus Resource Center. The first data was compiled on 26 March 2021 and will be updated regularly.

From April 2020 participants from our main Understanding Society sample have been asked to complete a short web-survey. This survey covers the changing impact of the pandemic on the welfare of UK individuals, families and wider communities. Participants complete a regular survey, which includes core content designed to track changes, alongside variable content adapted as the coronavirus situation develops. Researchers will be able to link the data from this web survey to answers respondents have given in previous (and future) waves of the annual Understanding Society survey.

This dataset is part of a larger research project investigating university students’ experiences during emergency remote teaching due to the COVID-19 pandemic. A longitudinal cohort study among university students from the Netherlands was performed during the academic year 2020/2021 with three points of measurement, i.e., t1 = November/December 2020, t2 = March 2021, and t3 = June/July 2021. Data were collected through online surveys programmed in Qualtrics.

The dataset includes data from 680 students who fully completed the survey at all three measurement points. Variables that are included in this dataset are at t1: demographic and study-related variables and at t1-t3: academic burnout, study engagement, education satisfaction, attitudes toward online education, online self-efficacy, and study effort.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This dataset includes 680 university students from the Netherlands describing their risk perception (both cognitive and affective - t1) of COVID-19, vaccination intention - t2, and vaccine uptake - t3. This study is part of a larger research project examining university students’ experiences during the COVID-19 outbreak. A longitudinal cohort study was performed during the academic year 2020/2021 with three points of measurement (November/December 2020, March 2021, June/July 2021) among Dutch university students. Students were included when they studied at a university that switched from offline teaching to online or blended teaching because of COVID-19 measures. Students who studied at an open university, studied parttime, or were aged above 30 years were excluded.

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 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 a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.

The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development.

Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)

Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)

Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)

Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)

Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)

Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)

Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)

Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)

Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)

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 and a step-by-step tutorial on how to use Hydrator) may be used.

Table 1. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development

Terminology

List of synonyms and terms

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

The COVID-19 Student Survey (CSS) was a multi-agency collaboration designed to examine student needs and health risk behaviors during the COVID-19 pandemic. The survey was intended to be administered online during school hours to students in grades 6 to 12 at all participating schools. Recruitment for the survey was initiated on February 18, 2021 and the survey was administered between March 8-26, 2021. The CSS was funded by the Washington State Health Care Authority (HCA), implemented by a team at the University of Washington (UW), with further partnership around content, design, and dissemination from the Office of Superintendent of Public Instruction (OSPI) and the Washington State Department of Health (DOH).

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

COVID-19 German Student Well-being Study (C19 GSWS)

Following the COVID-19 International Student Well-being Study (C19 ISWS; survey phase: May 13^th, 2020 to May 29^th, 2020 in 27 European countries coordinated by the University of Antwerp), well-being of university students during the COVID-19 pandemic continued to be the focus of the collaborative COVID-19 German Student Well-being Study (C19 GSWS) which was conducted at five universities in Germany.

The five German universities taking part in the study were the Charité – Universitätsmedizin Berlin (PI: Prof. Christiane Stock), the University of Bremen (PI: Dr. Heide Busse), Heinrich-Heine-University Duesseldorf (PI: Prof. Claudia Pischke), University of Siegen (PI: Prof. Claus Wendt) and Martin-Luther University Halle-Wittenberg (PI: Prof. Rafael Mikolajczyk).

The following research questions were addressed:

- How did university students' (physical and socioeconomic) living conditions and academic workload change during the COVID-19 pandemic?

- How were living and study conditions associated with mental health outcomes among university students during the COVID-19 pandemic?

- How were living conditions and academic workload associated with health behaviours (e.g., substance use) among university students during the pandemic?

- Which attitudes towards COVID-19 vaccination and determinants of vaccination behavior were prevalent r among university students?

To answer the research questions, an online survey among university students was conducted at all participating universities from October 27^th, 2021 to November 14^th, 2021. The resulting data allow for a description of living conditions, as well as well-being, during the ongoing COVID-19 pandemic in German university student populations.

Information about C19 ISWS on Zenodo:

https://zenodo.org/communities/c19-isws/?page=1&size=20

COVID-QU-Ex Dataset

The researchers of Qatar University have compiled the COVID-QU-Ex dataset, which consists of 33,920 chest X-ray (CXR) images including: - 11,956 COVID-19 - 11,263 Non-COVID infections (Viral or Bacterial Pneumonia) - 10,701 Normal Ground-truth lung segmentation masks are provided for the entire dataset. This is the largest ever created lung mask dataset.

If you use COVID-QU-Ex Dataset in your research, please consider to cite the publications/dataset below: [1] A. M. Tahir, M. E. H. Chowdhury, A. Khandakar, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Madeed, S. Mahmud, M. Ezeddin, K. Hameed, and T. Hamid, “COVID-19 Infection Localization and Severity Grading from Chest X-ray Images”, Computers in Biology and Medicine, vol. 139, p. 105002, 2021, https://doi.org/10.1016/j.compbiomed.2021.105002. [2] Anas M. Tahir, Muhammad E. H. Chowdhury, Yazan Qiblawey, Amith Khandakar, Tawsifur Rahman, Serkan Kiranyaz, Uzair Khurshid, Nabil Ibtehaz, Sakib Mahmud, and Maymouna Ezeddin, “COVID-QU-Ex .” Kaggle, 2021, https://doi.org/10.34740/kaggle/dsv/3122958. [3] T. Rahman, A. Khandakar, Y. Qiblawey A. Tahir S. Kiranyaz, S. Abul Kashem, M. Islam, S. Al Maadeed, S. Zughaier, M. Khan, M. Chowdhury, "Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images," Computers in Biology and Medicine, p. 104319, 2021, https://doi.org/10.1016/j.compbiomed.2021.104319. [4] A. Degerli, M. Ahishali, M. Yamac, S. Kiranyaz, M. E. H. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, "Covid-19 infection map generation and detection from chest X-ray images," Health Inf Sci Syst 9, 15 (2021), https://doi.org/10.1007/s13755-021-00146-8. [5] M. E. H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. A. Emadi, M. B. I. Reaz, M. T. Islam, "Can AI Help in Screening Viral and COVID-19 Pneumonia?," IEEE Access, vol. 8, pp. 132665-132676, 2020, https://doi.org/10.1109/ACCESS.2020.3010287.

To the best of our knowledge, this is the first study that utilizes both lung and infection segmentation to detect, localize and quantify COVID-19 infection from X-ray images. Therefore, it can assist the medical doctors to better diagnose the severity of COVID-19 pneumonia and follow up the progression of the disease easily.

The experiments were conducted on two CXR sets, where each set is divided into train, validation and test sets: 1) Lung Segmentation Data Entire COVID-QU-Ex dataset (33,920 CXR images with corresponding ground-truth lung masks) 2) COVID-19 Infection Segmentation Data A subset of COVID-QU-Ex dataset (1,456 Normal and 1,457 Non-COVID-19 CXRs with corresponding lung mask, plus 2,913 COVID-19 CXRs with corresponding lung mask from COVID-QU-Ex dataset and corresponding infections masks from QaTaCov19 dataset).

References

In COVID-QU-Ex, the X-ray images are collected from the following repositories and studies: • COVID-19 Samples: [1- 7]. • Non-COVID Samples: [8- 10]. • Normal Samples: [8- 10].

[1] QaTa-COV19 Database. https://www.kaggle.com/aysendegerli/qatacov19-dataset. Accessed 14 March 2021. [2] Covid-19-image-repository. Available: https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png. Accessed 14 March 2021. [3] Eurorad. Available: https://www.eurorad.org/. Accessed 14 March 2021. [4] Covid-chestxray-dataset. Available: https://github.com/ieee8023/covid-chestxray-dataset. Accessed 14 March 2021. [5] COVID-19 DATABASE. Available: https://www.sirm.org/category/senza-categoria/covid-19/. Accessed 14 March 2021. [6] Kaggle. (2020). COVID-19 Radiography Database. Available: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. Accessed 14 March 2021. [7] GitHub. (2020). COVID-CXNet. Available: https://github.com/armiro/COVID-CXNet. Accessed 14 March 2021. [8] RSNA Pneumonia Detection Challenge. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. Accessed 14 March 2021. [9] Chest X-Ray Images (Pneumonia). Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed 14 March 2021. [10] Medical Imaging Databank of the Valencia Region. PadChest: A large chest x-ray image dataset with multi-label annotated reports. Available: https://bimcv.cipf.es/bimcv-projects/padchest/. Accessed 14 March 2021.

This is a study on healthcare workers at the University of North Carolina Hospital system conducted during the COVID-19 pandemic in 2020-2021. This includes responses to survey questions on occupation, living situation, mental health, physical health, prior COVID-19 infection, and vaccination status. As the data are identifiable, we cannot release them publicly. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: These data are owned by the University of North Carolina at Chapel Hill. Contact Dr. Emily Ciccone ciccone@med.unc.edu with inquiries. Format: This dataset includes data on healthcare workers, including questionnaire responses and data from wearable tracking devices. These data are sensitive and participants are potentially identifiable.

Dataset includes README file that describes all datapoints.

The Bureau of Justice Statistics (BJS) began designing the National Prisoner Statistics Program - Coronavirus Pandemic Supplement (NPS-CPan) in spring 2020, while simultaneously adding questions on the effects of COVID-19 to its Annual Survey of Jails and Annual Surveys of Probation and Parole. The NPS-CPan was conducted from April to October, 2021 by Abt Associates, Inc. on behalf of BJS, as part of the existing multiyear award to collect annual National Prisoner Statistics (NPS-1b) and National Corrections Reporting Program (NCRP) data. The NPS-CPan was designed to be fielded a single time, and was administered to the 50 state departments of corrections (DOCs) and the federal Bureau of Prisons (BOP), which is also responsible for housing felons sentenced in the District of Columbia. Respondents were asked to complete a survey requesting details on the monthly custody prison population, admissions, and releases of prisoners from January 2020 to February 2021 and counts and demographic distributions of prisoners who tested positive for and who died from COVID-19. In addition, questions covered policies and practices used by states and the BOP to mitigate transmission of the virus, expedite release of prisoners, and determine the process by which staff and prisoners were vaccinated in early 2021. This 14-month survey period allowed BJS to track monthly trends in admissions and custody populations immediately prior to widespread COVID-19 infections in the United States, as well as capture the introduction of vaccines to prison systems.

AimTo provide estimates of the distribution of alcohol-related problems in a national sample of college and university students in 2021, i.e., during the COVID-19 pandemic, in comparison with pre-pandemic data from 2018.DesignLongitudinal data from linkage of two recent national health surveys from 2018 to 2021.SettingStudents in higher education in Norway (the SHoT-study).Participants8,287 fulltime students (72.5% women, 27.6% men) that were 18 years or more at the time of the first survey in 2018, and 21 years or more at the time of the second survey in 2021.MeasurementsThe Alcohol Use Disorders Identification Test (AUDIT) was used to assess potential alcohol-related problems.Findings37.0% of male students and 24.1% of female students reported either risky, harmful, or dependent alcohol use in 2021, compared with 55.0% of male students and 43.6% of female students in 2018. This decrease in alcohol-related problems was most pronounced for dependent alcohol use, where we observed a 57% relative reduction among male students (from 3.5% in 2018 to 1.5% in 2021) and a 64% relative reduction among female students (from 1.4% in 2018 to 0.5% in 2021).ConclusionsThe present study demonstrated a sharp decline in alcohol-related problems among students during the COVID-19 pandemic, that were present across gender, age groups, and geographical study locations. Universal preventive measures to limit students' alcohol use should be considered when restrictions related to the pandemic is lifted.

Florida COVID-19 Case Line data, exported from the Florida Department of Health GIS Layer on date seen in file name. Archived by the University of South Florida Libraries, Digital Heritage and Humanities Collections. Contact: LibraryGIS@usf.edu. Starting on 4/6/2021, the Florida Department of Health (FDOH) changed the way they provide COVID-19 caseline data. Beginning with this date the caseline data is being archived as two separate files, one for 2020 and one for 2021. The 2021 file will only include data from 1/1/2021 onward. In addition, FDOH has added two Object ID fields to their dataset. These caseline data are being preserved as they are provided by the FDOH, with a daily archive captured by the USF Libraries DHHC.Please Cite Our GIS HUB. If you are a researcher or other utilizing our Florida COVID-19 HUB as a tool or accessing and utilizing the data provided herein, please provide an acknowledgement of such in any publication or re-publication. The following citation is suggested: University of South Florida Libraries, Digital Heritage and Humanities Collections. 2021. Florida COVID-19 Hub. Available at https://covid19-usflibrary.hub.arcgis.com/. https://doi.org/10.5038/USF-COVID-19-GISLive FDOH Data Source: https://www.arcgis.com/home/item.html?id=7a0c74a551904761812dc6b8bd620ee1 or Direct Download at: https://open-fdoh.hub.arcgis.com/datasets/7a0c74a551904761812dc6b8bd620ee1_0.

Archives for this data layer begin on 5/11/2020. Archived data was exported directly from the live FDOH layer into the archive by the University of South Florida Libraries - Digital Heritage and Humanities Collection.For data definitions please visit the following box folder: https://usf.box.com/s/vfjwbczkj73ucj19yvwz53at6v6w614hData definition files names include the relative date they were published. The below information was taken from ancillary documents associated with the original layer from the Florida Department of Health. This data table represents all laboratory-confirmed cases of COVID-19 in Florida tabulated from the previous day's totals by the Florida Department of Health. Persons Under Investigation/Surveillance (PUI):Essentially, PUIs are any person who has been or is waiting to be tested. This includes: persons who are considered high-risk for COVID-19 due to recent travel, contact with a known case, exhibiting symptoms of COVID-19 as determined by a healthcare professional, or some combination thereof. PUI’s also include people who meet laboratory testing criteria based on symptoms and exposure, as well as confirmed cases with positive test results. PUIs include any person who is or was being tested, including those with negative and pending results.All PUIs fit into one of three residency types:1. Florida residents tested in Florida2. Non-Florida residents tested in Florida 3. Florida residents tested outside of Florida Florida Residents Tested Elsewhere: The total number of Florida residents with positive COVID-19 test results who were tested outsideof Florida, and were not exposed/infectious in Florida. Non-Florida Residents Tested in Florida: The total number of people with positive COVID-19 test results who were tested, exposed, and/or infectious while in Florida, but are legal residents of another state.Table Guide for Records of Confirmed Positive Cases of COVID-19"County": The Florida county where the individual with COVID-19's case has been processed. "Jurisdiction" of the case:"FL resident" -- a resident of Florida"Non-FL resident" -- someone who resides outside of Florida "Travel_Related": Whether or not the positive case of COVID-19 is designated as related to recent travel by the individual. "No" -- Case designated as not being a risk related to recent travel"Unknown" -- Case designated where a travel-related designation has not yet been made."Yes" -- Case is designated as travel-related for a person who recently traveled overseas or to an area with community"Origin": Where the person likely contracted the virus before arriving / returning to Florida."EDvisit": Whether or not an individual who tested positive for coronavirus visited and was admitted to an Emergency Department related to health conditions surrounding COVID-19."No" -- Individual was not admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Unknown" -- It is unknown whether the individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19"Yes" -- Individual was admitted to an emergency department relating to health conditions surrounding the contraction of COVID-19“Hospitalized”: Whether or not a patient who receives a positive laboratory confirmed test for COVID-19 receives inpatient care at a hospital at any time during illness. These people may no longer be hospitalized. This information does not indicate that a COVID-19 positive person is currently hospitalized, only that they have been hospitalized for health conditions relating to COVID-19 at some point during their illness. "No" -- Individual was not admitted for inpatient care at a hospital at any time during illness "Unknown" -- It is unknown whether the individual was admitted for inpatient care at a hospital at any time during illness "Yes" -- Individual was admitted for inpatient care at a hospital at some point during the illness "Died": Whether or not the individual who tested positive for COVID-19 died as a result of health complications from the viral infection. "NA" -- Not applicable / resident has not died "Yes" -- Individual died of a health complication resulting from COVID-19 "Contact": Whether the person contracted COVID-19 from contact with current or previously confirmedcases."No" -- Case with no known contact with current or previously confirmed cases"Yes" -- Case with known contact with current or previously confirmed cases"Unknown" -- Case where contact with current or previous confirmedcases is not known or under investigation"Case_": The date the positive laboratory result was received in the Department of Health’s database system and became a “confirmed case.” This is not the date a person contracted the virus, became symptomatic, or was treated. Florida does not create a case or count suspected/probable cases in the case counts without a confirmed-positive lab result. "EventDate": When the individual reported likely first experiencing symptoms related to COVID-19. "ChartDate": Also the date the positive laboratory result for an individual was received in the Department ofHealth’s database system and became a recorded, “confirmed case” of COVID-19 in the state. Data definitions updated by the FDOH on 5/13/2020.

These files contain the raw datasets produced by University of Tulsa McFarlin Library systems tracking daily, monthly, bi-annual, or annual interactions with library patrons. Files do not contain any identifying information about library patrons in compliance with state and federal laws regarding library patron privacy and student information privacy. These files are not the final aggregate datasets utilized for this project. Files may have irregular naming conventions, dates, formats, entry methods, missing data, averaged data, or other inconsistencies because each library system outputs data in a different manner. Aggregate and formatted versions of the data are available within the University of Tulsa McFarlin Library and COVID Data, 2020-2021 Dataverse: https://dataverse.harvard.edu/dataverse/McFarlinandCOVID. All files are housed in Excel spreadsheet formats and may contain multiple tabs.

When the COVID-19 pandemic began, U.S. college students reported increased anxiety and depression. This study examines mental health among U.S college students during the subsequent 2020–2021 academic year by surveying students at the end of the fall 2020 and the spring 2021 semesters. Our data provide cross-sectional snapshots and longitudinal changes. Both surveys included the PSS, GAD-7, PHQ-8, questions about students’ academic experiences and sense of belonging in online, in-person, and hybrid classes, and additional questions regarding behaviors, living circumstances, and demographics. The spring 2021 study included a larger, stratified sample of eight demographic groups, and we added scales to examine relationships between mental health and students’ perceptions of their universities’ COVID-19 policies. Our results show higher-than-normal frequencies of mental health struggles throughout the 2020–2021 academic year, and these were substantially higher for female college students, but by spring 2021, the levels did not vary substantially by race/ethnicity, living circumstances, vaccination status, or perceptions of university COVID-19 policies. Mental health struggles inversely correlated with scales of academic and non-academic experiences, but the struggles positively correlated with time on social media. In both semesters, students reported more positive experiences with in-person classes, though all class types were rated higher in the spring semester, indicating improvements in college students’ course experiences as the pandemic continued. Furthermore, our longitudinal data indicate the persistence of mental health struggles across semesters. Overall, these studies show factors that contributed to mental health challenges among college students as the pandemic continued.

This dataset is part of a research project investigating university students’ experiences during emergency remote education due to the COVID-19 pandemic. A longitudinal cohort study among university students from the Netherlands was conducted during the academic year 2020/2021 with three points of measurement, i.e., t1 = November/December 2020, t2 = March 2021, and t3 = June/July 2021. Data were collected through online surveys programmed in Qualtrics. The dataset includes data from 892 students who completed the survey at t2. More information about the research project, the dataset, the measured concepts/variables, and all included items can be found here: https://doi.org/10.25397/eur.23276108