Estimated percentage of the population in England who have tested positive for COVID-19 during the survey period from the Coronavirus (COVID-19) Infection Survey.

Survey of staff and students at the University of Edinburgh related to their participation in a routine, asymptomatic Covid-19 workplace testing pilot. 522 participants completed a pilot survey in April 2021 and 1,750 completed the main survey (November 2021). Surveys explored: the acceptability of regular PCR testing among students and staff, particularly involving an approach that was less invasive than nasopharyngeal swabbing; barriers and facilitators to participating in a regular university testing programme, including in the context of other testing methods being available; and whether participation in such a programme changed adherence to public health guidelines.

As of March 2022, a survey conducted on COVID-19 in Thailand revealed that approximately 41.1 percent of respondents stated that they got tested for COVID-19 once in a while. Meanwhile, about 23.8 percent of Thais answered that they had never done any COVID-19 tests before.

During a April 2022 survey, 80.6 percent of surveyed small businesses in the United States claimed that they did not require their employees to have a negative COVID-19 test result before physically coming in to work. In comparison, 6.2 percent of respondents said that they did require their employees to have a negative test result.

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

The purpose of this dataset is to understand the prevalence of COVID-19 in the UK population, including swab results, antibody tests and demographic information.

The National Ambulatory Medical Care Survey (NAMCS), conducted by the National Center for Health Statistics (NCHS), collects data on visits to physician offices to describe patterns of ambulatory care delivery in the United States. As part of NAMCS, the Physician Induction Interview collects information about practice characteristics at physician offices. Partway through the 2020 NAMCS, NCHS added questions to the Physician Induction Interview to assess physician experiences related to COVID-19 in office-based settings. The data include nationally representative estimates of experiences related to COVID-19 among office-based physicians in the United States, including: shortages of personal protective equipment (PPE) in the past 3 months; the ability to test for COVID-19 in the past 3 months; providers testing positive for COVID-19 in the past 3 months; turning away COVID-19 patients in the past 3 months; and telemedicine or telehealth technology use before and after March 2020. Estimates were derived from interviews with physicians in periods 3 and 4 of 2020 NAMCS and periods 1 through 4 of 2021 NAMCS, which occurred between December 15, 2020 and May 6, 2022. The data are considered preliminary, and the results may change with the final data release.

As of August 2020, over half of survey participants found the regular coronavirus (COVID-19) testing at workplaces important in order to stop the spread of the virus. On the other hand, one-fifth of respondents did not find it necessary to conduct PCR tests at their workplace.

United States SB: IF: NF: Availability of COVID-19 Tests for Employees data was reported at 1.800 % in 28 Feb 2022. This records an increase from the previous number of 1.200 % for 21 Feb 2022. United States SB: IF: NF: Availability of COVID-19 Tests for Employees data is updated weekly, averaging 1.800 % from Feb 2022 (Median) to 28 Feb 2022, with 3 observations. The data reached an all-time high of 2.500 % in 14 Feb 2022 and a record low of 1.200 % in 21 Feb 2022. United States SB: IF: NF: Availability of COVID-19 Tests for Employees data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S035: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

Replication datasets and code for manuscript published in PLOS One

The COVID-19 pandemic that spread across the world at the beginning of 2020 was not only a big threat to public health, but also to the entire youth and amateur sports industry. During a May 2020 survey in the United States, some 43 percent of respondents stated that it was very important to test a child's coaches for COVID-19 before their children started competing in organized sports again.

Outcome frequencies for COVID-19 vaccine intentions, COVID-19 testing perceptions, and level of trust in various sources for COVID-19 information by language other than English (LOE) status.

United States SB: OH: NF: Availability of COVID-19 Tests for Employees data was reported at 2.700 % in 21 Feb 2022. This records a decrease from the previous number of 4.500 % for 14 Feb 2022. United States SB: OH: NF: Availability of COVID-19 Tests for Employees data is updated weekly, averaging 3.600 % from Feb 2022 (Median) to 21 Feb 2022, with 2 observations. The data reached an all-time high of 4.500 % in 14 Feb 2022 and a record low of 2.700 % in 21 Feb 2022. United States SB: OH: NF: Availability of COVID-19 Tests for Employees data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S037: Small Business Pulse Survey: by State: Midwest Region: Weekly, Beg Monday (Discontinued).

United States SB: NF: Availability of COVID-19 Tests for Employees data was reported at 1.100 % in 11 Apr 2022. This records a decrease from the previous number of 1.200 % for 04 Apr 2022. United States SB: NF: Availability of COVID-19 Tests for Employees data is updated weekly, averaging 2.200 % from Feb 2022 (Median) to 11 Apr 2022, with 9 observations. The data reached an all-time high of 5.900 % in 14 Feb 2022 and a record low of 1.000 % in 14 Mar 2022. United States SB: NF: Availability of COVID-19 Tests for Employees data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S035: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020. Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5. Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

Abstract

Nigeria was among the first few countries in Sub-Saharan Africa to identify cases of COVID-19. Reported cases and fatalities have been increasing since it was first identified. The government implemented strict measures to contain the spread of this virus (such as travel restrictions, school closures and home-based work). While the Government is implementing these containment measures, it is important to understand how households in the country are affected and responding to the evolving crises, so that policy responses can be designed well and targeted effectively to reduce the negative impacts on household welfare.

The objective of Nigeria COVID-19 NLPS is to monitor the socio-economic effects of this evolving COVID-19 pandemic in real time. These data will contribute to filling critical gaps in information that could be used by the Nigerian government and stakeholders to help design policies to mitigate the negative impacts on its population. The Nigeria COVID-19 NLPS is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis.

The households were drawn from the sample of households interviewed in 2018/2019 for Wave 4 of the General Household Survey—Panel (GHS-Panel). The extensive information collected in the GHS-Panel just over a year prior to the pandemic provides a rich set of background information on the Nigeria COVID-19 NLPS households which can be leveraged to assess the differential impacts of the pandemic in the country.

Each month, the households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labour sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 4 of the GHS-Panel conducted in 2018/19 served as the frame for the Nigeria COVID-19 NLPS survey. The GHS-Panel sample includes 4,976 households that were interviewed in the post-harvest visit of the fourth wave in January/February 2019. This sample of households is representative nationally as well as across the 6 geopolitical Zones that divide up the country. In every visit of the GHS-Panel, phone numbers are collected from interviewed households for up to 4 household members and 2 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and the GHS-Panel households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Nigeria.

Among the 4,976 households interviewed in the post-harvest visit of the GHS-Panel in 2019, 4,934 (99.2%) provided at least one phone number. Around 90 percent of these households provided a phone number for at least one household member while the remaining 10 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the Nigeria COVID-19 NLPS.

To obtain a nationally representative sample for the Nigeria COVID-19 NLPS, a sample size of approximately 1,800 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Drawing from prior telephone surveys in Nigeria, a final contact plus response rate of 60% was assumed, implying that the required sample households to contact in order to reach the target is 3,000.

3,000 households were selected from the frame of 4,934 households with contact details. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted. The balanced sampling approach enables selection of a random sample that still retains the properties of the frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance (Tille, 2006). The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behaviour and social distancing; access to basic services; employment; income loss; food security; concerns; coping/shocks; and social safety nets.

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; employment (including non-farm enterprise and agricultural activity); other income; food security; and social safety nets.

ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; housing; employment (including non-farm enterprise and agricultural activity); other income; coping/shocks; and social safety nets.

ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; credit; employment (including non-farm enterprise, crop farming and livestock); food security; income changes; concerns; and social safety nets.

ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise and agricultural activity); and other income.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise); COVID testing and vaccination; and other income.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise); food security; concerns; and safety nets.

ROUND 8: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; employment (including non-farm enterprise and agriculture); and coping/shocks.

ROUND 9: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; early childhood development, access to basic services, employment (including non-farm enterprise and agriculture); and income changes.

ROUND 10: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise and agricultural activity); concerns and COVID testing and vaccination.

ROUND 11: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; credit; access to basic services; education; employment (including non-farm enterprise); safety nets; youth contact details; and phone signal.

ROUND 12: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on youth aspirations and employment; and COVID vaccination.

Cleaning operations

COMUPTER ASSISTED TELEPHONE INTERVIEW (CATI): The Nigeria COVID-19 NLPS exercise was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given two tablets, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CATI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication

IntroductionRapid advances in personalized medicine and direct-to-consumer genomic applications could increase the risk that physicians will apply genomic results inappropriately. To address a persistent lack of understanding of genomics, we implemented a pathology-supported genetic testing (PSGT) approach, guided by insights from a clinician needs assessment conducted in 2010.MethodsFindings from the previous clinician survey were used to develop a new patient screening tool that integrates non-communicable disease (NCD) and post-COVID-19 care pathways. In parallel to the application of this solution for stratification of patients in different treatment groups, an updated version of the original survey questionnaire was used to reassess the knowledge and willingness of healthcare professionals to apply PSGT.ResultsThirty-six respondents completed the revised needs assessment survey in October 2022, while attending a genomics session at the Annual General Practitioner Congress, Stellenbosch University, South Africa. Nearly 89% of the respondents reported having insufficient knowledge to offer genetic testing; 80% were supportive of using PSGT to differentiate inherited from lifestyle- or therapy-associated NCDs and 83.3% supported integrating wellness screening with genetic testing to identify high-risk individuals.DiscussionIt appears that while clinicians are interested in learning about genomics, they continue to report significant knowledge deficits in this area, highlighting the need for targeted clinician training and tools like multidisciplinary NCD-COVID pathway analysis to improve clinical decision-making. The co-development of a genomic counseling report for ongoing studies, guided the selection of Long COVID patients for whole-genome sequencing across the illness and wellness domains.

This public use microdata file (PUMF) provides researchers access to data on the distribution of SARS-CoV-2 lineages detected in the Canadian provinces between May and September 2022. Samples were collected as part of the Canadian COVID-19 Antibody and Health Survey (CCAHS), Cycle 2. The PUMF consists of a subset of participants of the CCAHS who submitted a positive PCR saliva sample over the course of the collection of the survey. The PUMF contains select demographic information including sex at birth, age group, province and the week of collection. The file also contains SARS-CoV-2 whole genome sequences and its associated data. The source survey for this PUMF, the CCAHS, Cycle 2, collected information in two parts. The first part is an electronic questionnaire about general health and exposure to COVID-19. The second part is two self-administered sample collections; an at-home finger-prick sample collection called a dried blood spot (DBS) sample, which was used to measure the presence of antibodies against SARS-CoV-2, the virus that causes COVID-19, from vaccination or prior infection. The second at-home collection was a saliva sample which was used to determine if there was a recent or current SARS-CoV-2 infection at the time of sampling, by testing for viral material in the sample using a polymerase chain reaction (PCR) test. Participants were asked to complete both sample collections as soon as possible after the questionnaire. The data can be used to: Estimate how many Canadians test positive for antibodies against COVID-19. By using each participant's DBS samples combined with their survey responses, we can determine how many Canadians have antibodies against COVID-19 due to infection, vaccination or both. Provide a platform to explore emerging public health issues; Assist in the development of programs and services to respond to the needs of the current pandemic. Identify the estimated prevalence of infection on any given day during May to August 2022 in Canada.

This file contains the results of the trend study dealing with rules of conduct. A representative group of people is regularly asked whether they comply with the rules of conduct that have been set in response to the Corona pandemic and what they think of the rules of conduct. Up to and including round 27 this was every three weeks, then every four weeks, and from round 33 every six weeks. There is an interval of almost four months between rounds 30 and 31. For more information about the research design: https://www.rivm.nl/gedragsonderzoek/trendonderzoek/backgroundinformation From round 36, corona-specific behavioral advice will no longer apply. There are still general behavioral recommendations to prevent respiratory infections. The file contains national (all rounds) and per Security Region (up to and including round 30) data on: - Compliance with the code of conduct - Support for the code of conduct - Self-efficacy (how difficult or easy do you find it to follow the code of conduct?) - Response effectiveness (does it help if everyone follows the rules of conduct?) - Social norm (do you see most people in your immediate environment follow the rules of conduct?) - Affective response (are you worried about the coronavirus?) - Willingness to vaccinate - Corona-related complaints - Psychological health (from round 31) - Loneliness (from round 31) Rules of conduct Compliance, support, self-efficacy, response effectiveness and social norm are queried for the following rules of conduct: - Curfew: stay at home after 9 p.m. have corona-related complaints (up to and including round 11) - Bij_klachten_blijf_terecht_thuis: stay at home if you have corona-related complaints, unless you have taken a negative test (from round 11) - Bij_klachten_laat_testen: do a corona test if you have complaints (at the GGD or a self-test ) - In case of_complaints_posttest_isolation: stay at home if you have a positive test result - Wear_facemask_in_public transport: wear a facemask in public transport - Wear_facemask_in_public_indoor spaces: wear a facemask in public indoor spaces - Wear_facemask_in_busy_places: wear a facemask in busy places outside - Cough_sneeze_in_elbow: if you have to cough or sneeze , then do this in the elbow - Keep_1_5m_distance: keep 1.5 meters away from others (compliance measured in different situations) - Receive_max_visitors_home: receive a maximum number of visitors at home (the recommended maximum varied over time, measured at the current time) advice) - Ventilate_house: provide sufficient fresh air in your home (usually or always ventilate and ventilate the room where you wash the most for 15 minutes or more twice or more per day) - Avoid_busy_places: avoid busy places or turn around if you do come to a busy place - Wash_your_hands_often: wash your hands regularly (more than 10 times a day) - Work_home: work (partly) at home if possible (advice varied over time) - Self-test_visit: do a self-test before visiting someone Data The file contains the following data: - Percentage or average - 95% confidence interval lower limit - 95% confidence interval upper limit - Change with respect to the previous measurement - Number of respondents in the sample By Security Region, per measurement period per indicator category per indicator Records The file contains the following set of records per questionnaire round: - A record for each Security Region in the Netherlands per indicator category per indicator (up to and including round 30, from round 31 only the Netherlands) - A record for total percentages in the Netherlands per indicator category per indicator per age category, by level of education and by gender indication (from round 32, participants whose gender is different from male or female participate. Because this is a small group of participants, this group is not shown in its own record, but they do count in the total) Indicator categories Compliance: Are the requested rules of conduct being observed (current behaviour)? Support base: To what extent do you support the code of conduct? Help_rules: Suppose everyone followed the government's rules of conduct, how well would that help to prevent the spread of the corona virus? Difficulty: How difficult or easy do you find it to comply with the rule of conduct? Close_environment: Do most of the people in the immediate environment of the surveyed follow the rules of conduct? Concerns: Are you concerned about the coronavirus? Vaccination readiness: Do you want to be vaccinated against covid? Complaints: Percentage of people with corona-related complaints Mental: Mental health in four categories based on the MHI-5. Loneliness: Loneliness in three categories based on De Jong Gierveld's abbreviated Loneliness Scale. Variables Description of the variables: Date_of_report: Date and time on which the data file was created by RIVM. Date_of_measurement: Date of the measurement started. The measurement duration is one week. The measurement therefore took place on the said date and six days afterwards. Wave Sequence number of the measurement Region_code: Netherlands and Security region code. The Netherlands has code NL00. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Safety Region_name: The Netherlands and name of the Security Region. This is the name of the Security Regions as used so far in various reports and reports by the RIVM, and may differ slightly from the naming as indicated in the CBS code list (see link above under variable Security_region_code). See also: https://www.rijksoverheid.nl/onderwerpen/veiligheidsregios-en-crisisbeheer/veiligheidsregios Subgroup_category: Dimensions into which the figures are broken down: - All (Total; no breakdown) - Gender (Male / Female) - Age (16 – 24 years old / 25 – 39 years old / 40 – 54 years old / 55 – 69 years old / 70+) - Education level (Low / Middle / High ) Subgroup: Name of the dimension (see Subgroup_category) Indicator_category: Categorization of the indicators: - Compliance - Support - Help_rules - Difficulty - Neighbor_environment - Worry - Willingness to vaccinate - Complaints - Psychological - Loneliness Indicator: Compliance, Support, Helping_rules, Effort and Neighbor_environment for the following rules of conduct: - Curfew - In case of_complaints_stay_at home - In case of_complaints_stay_right_at home - In case of_complaints_late_tests - In case of_complaints_posttest_isolation - Wear_facemask_in_OV - Wear_mouth cap_in_public_interior_spaces - Wear_mask_on_busy_places - Cough_sneeze_in_elbow - Keep_1_5m_distance - Receive_max_visitors_at home - Worked_home hours: Average percentage of hours a participant works at home of the hours a participant works - Ventilating_house - Avoid_busy_places - Wash_your_hands_often_your_hands - Work_home - Self-test_visit Concerns: - Concerns_about_Coronavirus Willingness to vaccinate (up to and including round 19): - Already_vaccinated - Yes - No - Don't know_Don't know Vaccinated_or_prepared - Yes (had at least one vaccination or willing to vaccinate) - No - Don't know (this answer option will no longer apply from round 31) Complaints at the time of completing the questionnaire: - At least_one_corona_related Sample_size: Number of respondents who have answered given to a question Figure_type: Grade (Percentage / Average) Value Calculated value of the Indicator Lower_limit 95% confidence interval lower limit Upper_limit 95% confidence interval upper limit Change_wrt_previous_measurement Significant (p < .05) difference compared to the previous measurement period (-1 = decrease / 0 = stayed the same / 1 = increased)