Before the coronavirus (COVID-19) pandemic, 17 percent of U.S. employees worked from home 5 days or more per week, a share that increased to 44 percent during the pandemic. The outbreak of the COVID-19 pandemic accelerated the remote working trend, as quarantines and lockdowns made commuting and working in an office close to impossible for millions around the world. Remote work, also called telework or working from home (WFH), provided a solution, with employees performing their roles away from the office supported by specialized technology, eliminating the commute to an office to remain connected with colleagues and clients. What enables working from home?

To enable remote work, employees rely on a remote work arrangements that enable hybrid work and make it safe during the COVID-19 pandemic. Technology supporting remote work including laptops saw a surge in demand, video conferencing companies such as Zoom jumped in value, and employers had to consider new communication techniques and resources. Is remote work the future of work?

The response to COVID-19 has demonstrated that hybrid work models are not necessarily an impediment to productivity. For this reason, there is a general consensus that different remote work models will persist post-COVID-19. Many employers see benefits to flexible working arrangements, including positive results on employee wellness surveys, and potentially reducing office space. Many employees also plan on working from home more often, with 25 percent of respondents to a recent survey expecting remote work as a benefit of employment. As a result, it is of utmost importance to acknowledge any issues that may arise in this context to empower a hybrid workforce and ensure a smooth transition to more flexible work models.

Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of metrics that were found to have the strongest relationships, with the base comparison metric of At risk employees (as a result of COVID-19) by employee residence. This dataset contains the following metrics:At risk employees (as a result of COVID-19) by employee residence - Shows the proportion of employees that are at risk of losing their jobs following the outbreak of COVID-19 - calculated based on the latest furloughing data from the ONS and the employee profile for each local authority. The data is derived from Wave 2 of the ONS Business Impact of Coronavirus Survey (BICS) which contains data on the furloughing of workers across UK businesses between March 23 to April 5, 2020 see https://www.ons.gov.uk/generator?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/furloughingofworkersacrossukbusinesses/23march2020to5april2020/574ca854&format=csv for details. This data includes responses from businesses that were either still trading or had temporarily paused trading. This has been mapped against the industrial composition of employee jobs at OA, LSOA, MSOA and Local Authority level to estimate which are most exposed to labour market risks associated with the Covid-19. The industrial composition of employee jobs is based on the employee place of residence rather than where they work. The data on the industrial composition of local areas comes from the 2011 Census Industrial classification, which is publicly accessible via NOMIS. The methodology is adapted from the RSA at-risk Local Authorities publication - https://www.thersa.org/about-us/media/2020/one-in-three-jobs-in-parts-of-britain-at-risk-due-to-covid-19-local-data-reveals This approach calculates the total number of employees at risk in each local area by identifying the number of employees in each industry in that area (based on employee residence) multiplied by the estimated percentage of those that have been furloughed on the Government's Coronavirus Job Retention Scheme (CJRS). The CRJS was set up by the Government specifically to prevent growing unemployment and the National Institute for Economic and Social Research (NIESR) has described furloughed workers as technically unemployed. It therefore looks to be the best available data with which to calculate medium-term employment risk as a result of Covid-19. This is then divided by the total number of employees in each local area (by place of residence) to calculate the percentage of employees at risk of losing their jobs. Note, employees in industry sectors which were not recorded in the ONS Business Impact of Coronavirus Survey (BICS) due to inadequate sample size have not been included in the numerator or denominator for this dataset - these include Agriculture, forestry and fishing, Mining and quarrying, Electricity, gas, steam and air conditioning supply, Financial and insurance activities, Real estate activities. Public administration and defence; compulsory social security and activities of households as employers; undifferentiated goods - and services - producing activities of households for own use. Social grade (N-SEC): 2. Lower managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 2. Lower managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Lower managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.IoD 2019 Education, Skills and Training Rank - The Indices of Deprivation (IoD) 2019 Education Skills and Training Domain measures the lack of attainment and skills in the local population. The indicators fall into two sub-domains: one relating to children and young people and one relating to adult skills. These two sub-domains are designed to reflect the 'flow' and 'stock' of educational disadvantage within an area respectively. That is the 'children and young people' sub-domain measures the attainment of qualifications and associated measures ('flow') while the 'skills' sub-domain measures the lack of qualifications in the resident working age adult population ('stock'). Children and Young People sub-domain includes: Key stage 2 attainment: The average points score/scaled score of pupils taking reading writing and mathematics Key stage 2 exams; Key stage 4 attainment: The average capped points score of pupils taking Key stage 4; Secondary school absence: The proportion of authorised and unauthorised absences from secondary school; Staying on in education post 16: The proportion of young people not staying on in school or non-advanced education above age 16 and Entry to higher education: The proportion of young people aged under 21 not entering higher education. The Adult Skills sub-domain includes: Adult skills: The proportion of working age adults with no or low qualifications women aged 25 to 59 and men aged 25 to 64; English language proficiency: The proportion of working age adults who cannot speak English or cannot speak English well women aged 25 to 59 and men aged 25 to 64. Data shows Average LSOA Rank, a lower rank indicates that an area is experiencing high levels of deprivation.Social grade (N-SEC): 1 Higher managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1 Higher managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Total annual household income estimate - Shows the average total annual household income estimate (unequivalised). These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.Household is not deprived in any dimension - Shows households which are not deprived on any of the four Census 2011 deprivation dimensions. The Census 2011 has four deprivation dimension characteristics: a) Employment: Any member of the household aged 16-74 who is not a full-time student is either unemployed or permanently sick; b) Education: No member of the household aged 16 to pensionable age has at least 5 GCSEs (grade A-C) or equivalent AND no member of the household aged 16-18 is in full-time education c) Health and disability: Any member of the household has general health 'not good' in the year before Census or has a limiting long term illness d) Housing: The household's accommodation is either overcrowded; OR is in a shared dwelling OR does not have sole use of bath/shower and toilet OR has no central heating. These figures are taken from responses to various questions in census 2011. Rate calculated as = (Household is not deprived in any dimension (census QS119))/(All households (census QS119))*100.Occupation group: Professional occupations - Shows the proportion of people in employment (aged 16-74) working in the Occupation group: Professional occupations. An individual's occupation group is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Professional occupations (census KS608))/(All usual residents aged 16 to 74 in employment the week before the census (census KS608))*100.Social grade (N-SEC): 1.2 Higher professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1.2 Higher professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Sport England Market Segmentation: Competitive Male Urbanites - proportion of people living in the area that are classified as Competitive Male Urbanites in the Sports Market Segmentation.Net annual household income estimate after housing costs - Shows the average annual household income estimate (equivalised to take into account variations in household size) after housing costs are taken into account. These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

Following the coronavirus (COVID-19) pandemic in 2020 and the transition to remote work, the volume of internet searches surrounding employee surveillance companies increased worldwide. In March 2023, the volume of internet searches for the software company DeskTime increased by 232 percent compared to March 2020.

BackgroundThere is an urgent need to better understand the factors that predict mental wellbeing in vocationally active adults during globally turbulent times.AimTo explore the relationship between psychological detachment from work (postulated as a key recovery activity from work) in the first national COVID-19 lockdown with health, wellbeing, and life satisfaction of working age-adults one year later, within the context of a global pandemic.MethodsWellbeing of the Workforce (WoW) was a prospective longitudinal cohort study, with two waves of data collection (Time 1, April-June 2020: T1 n = 337; Time 2, March-April 2021: T2 = 169) corresponding with the first and third national COVID-19 lockdowns in the UK. Participants were >18 years, who were employed or self-employed and working in the UK. Descriptive and parametric (t-tests and linear regression) and nonparametric (chi square tests) inferential statistics were employed.ResultsRisk for major depression (T1: 20.0% to T2: 29.0%, p = .002), poor general health (T1: 4.7% to T2: 0%, p = .002) and poor life satisfaction (T1: 15.4% to T2: 25.4%, p = .002) worsened over time, moderate-to-severe anxiety remained stable (T1: 26.1% to T2: 30.2%, p = .15). Low psychological detachment from work was more prevalent in the first wave (T1: 21.4% and T2: 16.0%), with a moderate improvement observed from T1 to T2 (t (129) = -7.09, p < .001). No differences were observed with work status (employed/self-employed), except for self-employed workers being more likely to report poor general health at T1 (16.1%, p = .002). Better psychological wellbeing, lower anxiety and higher life satisfaction at T2 were observed in those who reported better psychological detachment from work at T1 (β = .21, p = .01; β = -.43, p < .001; β = .32, p = .003, respectively), and in those who improved in this recovery activity from T1 to T2 (β = .36, p < .001; β = -.27, p < .001; β = .27, p = .008, respectively), controlling for age, gender and ethnicity.ConclusionThe ability to psychologically detach from work during the first pandemic lockdown, and improvement in this recovery activity over time, predicted better mental wellbeing and quality of life in vocationally active adults after one year of a global crisis, irrespective of work status. Interventions to encourage workers to psychologically detach from work may help to support employee wellbeing at all times, not only in the extreme circumstances of pandemics and economic uncertainty.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

In July 2020, nearly ** percent of private enterprises in Tunisia did not recur to any employment adjustment following the coronavirus (COVID-19) crisis. In the same month, around ** percent of businesses reduced working hours, almost **** percent declared to have hired someone, while almost ** percent fired personnel. Compared to **********, a period that particularly suffered the impact of the pandemic, redundancies increased significantly in July.

As of January 2024, several major technology companies, including Google, Amazon, Meta, and Apple, have implemented return-to-office mandates requiring employees to be in the office at least three days per week. Interestingly, Zoom, a company that played a significant role in facilitating work-from-home activities during the COVID-19 pandemic, has announced a return-to-office mandate of its own requiring employees to work from the office twice per week. In contrast, X (formerly Twitter) adopted an office-only policy for their employees since Elon Musk acquired Twitter in 2022, requiring all X employees to work from the office the entire work week.

Replication package for "Business disruptions from social distancing"

Please cite as

Koren, Miklós and Rita Pető. 2020. "Replication package for «Business disruptions from social distancing»" [dataset] Zenodo. http://doi.org/10.5281/zenodo.4012191

License and copyright

All text (*.md, *.txt, *.tex, *.pdf) are CC-BY-4.0. All code (*.do, Makefile) are subject to the 3-clause BSD license. All derived data (data/derived/*) are subject to Open Database License. Please respect to copyright and license terms of original data vendors (data/raw/*).

Data Availability Statements

The mobility data used in this paper (SafeGraph 2020) is proprietary, but may be obtained free of charge for COVID-19-related research from the COVID-19 Consortium. The authors are not affiliated with this consortium. Researchers interested in access to the data can apply at https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com). After signing a Data Agreement, access is granted within a few days. The Consortium does not require coauthorship and does not review or approve research results before publication. Datafiles used: /monthly-patterns/patterns_backfill/2020/05/07/12/2020/02/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released May 7, 2020), /monthly-patterns/patterns/2020/06/05/06/patterns-part[1-4].csv.gz (Monthly Places Patterns for February 2020, released June 5, 2020) and /core/2020/06/Core-USA-June2020-Release-CORE_POI-2020_05-2020-06-06.zip (Core Places for June 2020, released June 6, 2020). The COVID-19 Consortium will keep these datafiles accessible for researchers. The authors will assist with any reasonable replication attempts for two years following publication.

All other data used in the analysis, including raw data, are available for reuse with permissive licenses. Raw data are saved in the folder data/raw/. The Makefile in each folder shows the URLs used to download the data.

SafeGraph

Citation

SafeGraph. "Patterns [dataset]"; 2020. Downloaded 2020-06-20.

License

Proprietary, see https://shop.safegraph.com/ or https://www.safegraph.com/covid-19-data-consortium (data manager: Ross Epstein, ross@safegraph.com)

O*NET

Citation

U.S. Department of Labor/Employment and Training Administration, 2020. "O*NET Online." Downloaded 2020-03-12.

License

CC-BY-4.0 https://www.onetonline.org/help/license

Current Employment Statistics

Citation

U.S. Bureau of Labor Statistics. 2020. "Current Employment Statistics." https://www.bls.gov/ces/ Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

National Employment Matrix

Citation

U.S. Bureau of Labor Statistics. 2018. "National Employment Matrix." https://www.bls.gov/emp/data/occupational-data.htm Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

Crosswalk

Citation

U.S. Bureau of Labor Statistics. 2019. "O* NET-SOC to Occupational Outlook Handbook Crosswalk." https://www.bls.gov/emp/classifications-crosswalks/nem-onet-to-soc-crosswalk.xlsx Downloaded 2020-03-15.

License

Public domain: https://www.bls.gov/bls/linksite.htm

American Time Use Survey

Citation

U.S. Bureau of Labor Statistics. 2018. “American Time Use Survey.” https://www.bls.gov/tus/.

We are using the following files:

• Respondent File
• Activity File
• Who File
• Replicate Weights
• Leave Module 2017-18

License

Data is in public domain.

County Business Patterns

Citation

U.S. Bureau of the Census. 2017. "County Business Patterns." Available at https://www.census.gov/programs-surveys/cbp.html

License

https://www.census.gov/data/developers/about/terms-of-service.html

Dataset list

Raw data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/raw/bls/industry-employment/ces.txt | BLS Current Employment Statistics | Public domain | Yes |

| data/raw/bls/atus/*.dat | BLS Time Use Survey | Public domain | Yes |

| data/raw/bls/employment-matrix/matrix.xlsx | BLS National Employment Matrix | Public domain | Yes |

| data/raw/bls/crosswalk/matrix.xlsx | ONET-SOC to Occupational Outlook Handbook Crosswalk | Public domain | Yes |

| data/raw/onet/*.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/raw/census/cbp/*.txt | County Business Patterns | Public domain | Yes |

| not-included/safegraph/02/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

| not-included/safegraph/05/*.csv| SafeGraph | Available with Data Agreement with SafeGraph | No |

Clean data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/clean/industry-employment/industry-employment.dta | BLS Current Employment Statistics | Public domain | Yes |

| data/clean/time-use/atus.dta | BLS Time Use Survey | Public domain | Yes |

| data/clean/employment-matrix/matrix.dta | BLS National Employment Matrix | Public domain | Yes |

| data/clean/onet/risks.csv | ONET Online | Creative Commons 4.0 | Yes |

| data/clean/cbp/zip_code_business_patterns.dta | County Business Patterns | Public domain | Yes |

Derived data

| Data file | Source | Notes | Provided |

|-----------|--------|----------|----------|

| data/derived/occupation/* | Various sources | Public domain | Yes |

| data/derived/time-use/atus_working_at_home_occupationlevel.dta | BLS Time Use Survey | Public domain | Yes |

| data/derived/crosswalk/* | Various sources | Public domain | Yes |

| not-included/safegraph/naics-zip-??.csv| SafeGraph | Available with Data Agreement with SafeGraph | Yes, with permission of SafeGraph |

| data/derived/visit/visit-change.dta| SafeGraph | Aggregated to 3-digit NAICS industries | Yes, with permission of SafeGraph |

Computational requirements

Software Requirements

• Stata (code was last run with version 16.1)
  • estout (from http://www.stata-journal.com/software/sj14-2/)
  • running make install from the root of the folder will install estout locally, and should be run once.

Portions of the code use bash scripting (make, wget, head, tail), which may require Linux or Mac OS X.

The entry point for analysis is analysis/Makefile, which can be run by GNU Make on any Unix-like system by

cd analysis
make

The dependence of outputs on code and input data is captured in the respective Makefiles.

We have used Mac OS X, but all the code should run on Linux and Windows platforms, too.

Hardware

The analysis takes a few minutes on a standard laptop.

Description of programs

1. Raw data are in data/raw/. This data is saved as it has been received from the data publisher, downloaded by the respective Makefiles. Each folder has a README.md with data citation and license terms.
2. Clean data are in data/clean/. Each folder has a Makefile that specifies the steps of data cleaning.
3. Analysis data are in data/derived/. Each folder has a Makefile that

When asked about remote working in a 2021 survey, over 70 percent of employed people in Finland said that they would like to work from home in the future. Working remotely part of the week was the most popular option with 36 percent, while 15 percent preferred working fully from home after the coronavirus (COVID-19) pandemic. However, about one fifth of Finns did not want to work from home.

In more than one half of surveyed companies in Russia, remote working was not possible or was not possible on paper prior to the COVID-19 pandemic, according to a survey conducted in the first quarter of 2021. Moreover, only ** percent of respondents confirmed that remote working arrangements were available to some extent in their companies prior to the lockdown.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set is the originally reported numbers by the facility. This data set may contain data anomalies due to data key entries.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This table contains data from the December release of Canadian Business Counts for 2007 until the latest complete year. The data includes the year, 2-digit North American Industry Classification System (NAICS) code, and a count of the number of businesses by number of employees. The table data shows the number of businesses categorized by the number of employees they have. Please ensure you read the notes provided below, as there is very important information on classification and comparability. NotesStatistics Canada advises users not to use these data as a time series. Further, the counts may reflect some of the business openings and closures caused by the COVID-19 pandemic, although they will not be fully represented as the evolving resumption or permanent closure of businesses may not yet be fully processed and confirmed by Statistics Canada's Business Register (The Daily — Canadian business counts, December 2021 (statcan.gc.ca)).Changes in methodology or in business industrial classification strategies used by Statistics Canada's Business Register can create increases or decreases in the number of active businesses reported in the data on Canadian business patterns. As a result, these data do not represent changes in the business population over time. Statistics Canada recommends users not to use these data as a time series. Beginning in December 2014, there were several important changes that were made:

The data appear in two separate series, one covering locations with employees, the other covering locations without employees. The second series corresponds to locations previously coded to the employment category called "indeterminate." A new North American Industrial Classification System (NAICS) category has been added to include locations that have not yet received a NAICS code: unclassified. It represents an additional 78,718 locations with employees and 313,107 locations without employees. The second series, locations without employees, also includes locations that were not previously included in tables but that meet the criteria used to define the Business Register coverage. The impact of the change will be the inclusion of approximately 600,000 additional locations.

Before 2014, the following notes apply:

The establishments in the "Indeterminate" category do not maintain an employee payroll, but may have a workforce which consists of contracted workers, family members or business owners. However, the Business Register does not have this information available, and has therefore assigned the establishments to an "Indeterminate" category. This category also includes employers who did not have employees in the last 12 months. Please note that the employment size ranges are based on data derived from payroll remittances. As such, it should be viewed solely as a business stratification variable. Its primary purpose is to improve the efficiency of samples selected to conduct statistical surveys. It should not be used in any manner to compile industry employment estimates. Employment, grouped in employment size ranges, is more often than not an estimation of the annual maximum number of employees. For example, a measure of "10 employees" could represent "10 full-time employees", "20 part-time employees" or any other combination.For more information refer to Statistics Canada's Definitions and Concepts used in Business Register.

BackgroundClinical signs of dysphagia, pancreatic achalasia, and esophagitis have been reported in patients with COVID-19. However, the causal relationship between COVID-19 and esophageal diseases is not clear. Therefore, we utilized Mendelian randomization to explore the potential association between COVID-19 and esophageal diseases.MethodsThe summary statistics for a Genome-wide association study (GWAS) were obtained from The COVID-19 Host Genetics Initiative, encompassing four types of COVID-19 as exposure: severe COVID-19, hospitalized COVID-19 versus ambulatory COVID-19, hospitalized COVID-19 versus uninfected, and confirmed COVID-19. Additionally, summary statistics for ten esophageal diseases as outcomes were sourced from the GWAS Catalog and FinnGen databases. Univariate Mendelian randomization (MR) analysis was utilized to thoroughly investigate and validate the potential causal association between COVID-19 and various esophageal conditions, including esophageal varices, Barrett’s esophagus, esophagitis, esophageal obstruction, esophageal ulcer, esophageal perforation, gastroesophageal reflux, congenital esophageal malformations, benign esophageal tumors, and esophageal adenocarcinoma.ResultsAn inverse variance-weighted (IVW) model was utilized for univariate Mendelian randomization (MR) analysis, which revealed that genetic liability in patients with confirmed COVID-19 was associated with esophageal obstruction (OR [95% CI]: 0.5275458 [0.2822400–0.9860563]; p-value = 0.0450699). Furthermore, a suggestive causal association was found between genetic liability and a reduced risk of benign esophageal tumors (OR [95% CI]: 0.2715453 [0.09368493–0.7870724]; p-value = 0.0163510), but with a suggestively increased risk of congenital esophageal malformations (OR [95% CI]: 6.959561 [1.1955828–40.51204]; p-value = 0.03086835). Additionally, genetic liability in hospitalized COVID-19 patients, compared to non-hospitalized COVID-19 patients, was suggestively associated with an increased risk of esophagitis (OR [95% CI]: 1.443859 [1.0890568–1.914252]; p-value = 0.01068201). The reliability of these causal findings is supported by Cochran’s Q statistic and the MR-Egger intercept test.ConclusionThe results of this study suggest the existence of a causal relationship between COVID-19 and esophageal diseases, highlighting differing risk effects of COVID-19 on distinct esophageal conditions.

This study aimed to investigate the potential relationship between multiple sclerosis (MS) and coronavirus disease 2019 (COVID-19) outcomes using Mendelian randomization analysis. Specifically, it evaluates whether genetic factors, including the single-nucleotide polymorphism (SNP) rs10191329, influence the susceptibility of MS patients to three COVID-19 outcomes [severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, hospitalized COVID-19, and severe COVID-19]. This study utilized genome-wide association study summary statistics from the International Multiple Sclerosis Genetics Consortium to conduct a Mendelian randomization analysis. SNPs strongly associated with MS were selected to examine their impact on COVID-19 outcomes. The analysis focused on identifying any causal associations between MS and COVID-19 severity, as well as assessing the role of interferon beta (IFNβ) treatment in modifying these outcomes. The results suggest a potential association between MS and an increased risk of COVID-19, but individuals carrying the rs10191329 SNP appeared less likely to develop severe COVID-19. This SNP, located within the DYSF-ZNF638 locus, may influence immune responses and MS severity, highlighting its relevance for personalized treatment strategies. Importantly, no significant causal relationship was found between IFNβ treatment and the three COVID-19 outcomes, indicating that the findings in treated patients differ from those observed in untreated patients. This suggests that IFNβ may offer protective effects against SARS-CoV-2 in MS patients. These findings underscore the importance of genetic factors, such as rs10191329, in shaping the clinical outcomes of MS patients in the context of COVID-19. Further research should explore targeted therapies and personalized approaches for managing MS during the ongoing pandemic.

In 2020, thinking of work after the pandemic, ** percent of German employees stated that they wanted to work from home as often as they did during the COVID-19 crisis. In contrast to this, ** percent did not want to work from home at all. The figures are based on an online survey conducted in Germany in 2020.

In the final week of March 2020, 2.8 million Canadians were away from their usual places of work and engaging in remote and/or telework to mitigate the spread of COVID-19 (Statistics Canada, 2020). The Government of Canada's Department of National Defence (DND) and the Canadian Armed Forces (CAF) were no exception, with most members from the regular force (Reg F), the primary reserve force (P Res), and the DND public service (DND PS) working from home. The COVID-19 Defence Team Survey was administered from April 29th, 2020, and May 22nd, 2020, to gain insight into work, health, and family-related challenges since the onset of the pandemic and change in work arrangements. Responses from five open-ended questions were qualitatively analyzed to determine general themes of concern regarding work, personal, and family related challenges, stress-management and coping strategies, and recommendations for improving the work situation and personal well-being. Given the different roles and conditions of employment, responses of the different groups or “components” of respondents (Reg F, P Res, DND PS) were compared to identify common and unique challenges to inform targeted organizational responses. A total of 26,207 members (Reg F = 13,668, 52.2%; P Res = 5,052, 19.3%; DND PS = 7,487, 28.6%) responded to the survey's five open-ended questions, which yielded a total of 75,000 open-ended responses. When asked about work-related challenges, respondents' most common challenges included dissatisfaction with technology/software, work arrangements, ergonomics, work-life balance, communication within the organization, and the uncertainties regarding career development. In terms of personal and/or family-related challenges, the most common challenges included social isolation, the impact of the pandemic on mental health, school closures and homeschooling, caring for vulnerable family members, and childcare concerns. The most common stress-management and coping strategies included exercise, spending time outdoors, communicating or spending time with family members, household chores/projects, mind-body wellness exercises, and playing games. The most common recommendations made by respondents to improve their work- or personal-related situations included improving technological capabilities, streamlining communication, providing hardware and software necessary to ensure comfortable ergonomics, the provision of flexibility in terms of telework schedules, return-to-work decisions, and the expansion of benefits and access to childcare services. In terms of differences among the components, DND PS personnel were most likely to report dissatisfaction with technological changes and ergonomics, and to recommend improving these technological limitations to maximize productivity. Reg F members, on the other hand, were most likely to recommend increased support and access to childcare, and both Reg F and P Res members were more likely to mention that increased benefits and entitlements in response to the COVID-19 pandemic would be ameliorative. The results of this study highlight several important facts about the impact of the COVID-19 pandemic on personnel working in large, diverse organizations. For example, advancements in organizational technological capabilities were highlighted herein, and these are likely to grow to maintain productivity should remote work come to be used more extensively in the long-term. This study also highlighted the importance of flexibility and accommodation in relation to individual needs – a trend that was already underway but has taken on greater relevance and urgency in light of the pandemic. This is clearly essential to the organization's role in supporting the well-being of personnel and their families. Clear and streamlined communication regarding organizational changes and support services is also essential to minimize uncertainty and to provide useful supports for coping with this and other stressful situations.

Vietnam Number of Enterprise: TS: Year to Date: SS: Employment Activities; Tourism data was reported at 3,090.000 Unit in Mar 2025. This records an increase from the previous number of 2,884.000 Unit for Feb 2025. Vietnam Number of Enterprise: TS: Year to Date: SS: Employment Activities; Tourism data is updated monthly, averaging 2,302.000 Unit from Jan 2018 (Median) to Mar 2025, with 87 observations. The data reached an all-time high of 4,848.000 Unit in Dec 2024 and a record low of 431.000 Unit in Jan 2018. Vietnam Number of Enterprise: TS: Year to Date: SS: Employment Activities; Tourism data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.O034: Company Statistics. [COVID-19-IMPACT]