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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

NOTE: This dataset has been retired and marked as historical-only. Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age. Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine. Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS). Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death. Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test. CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset. Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000. Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people. Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population. Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019. All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week. Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined. For all datasets related to COVID-19, see https://data.cityofchic

Context

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - WHO

People can catch COVID-19 from others who have the virus. This has been spreading rapidly around the world and Italy is one of the most affected country.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak. - TIME

Content

This dataset is from https://github.com/pcm-dpc/COVID-19 collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale

This dataset has two files

• covid19_italy_province.csv - Province level data of COVID-19 cases
• covid_italy_region.csv - Region level data of COVID-19 cases

Acknowledgements

Data is collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale and is uploaded into this github repo.

Dashboard on the data can be seen here. Picture courtesy is from the dashboard.

Inspiration

Insights on * Spread to various regions over time * Try to predict the spread of COVID-19 ahead of time to take preventive measures

In collaboration with the Public Health Agency of Canada (PHAC), this table provides Canadians and researchers with data to monitor only the confirmed cases of coronavirus (COVID-19) in Canada. This table will provide an aggregate summary of the data available in the publication 13-26-0003.

As the UK went into the first lockdown of the COVID-19 pandemic, the team behind the biggest social survey in the UK, Understanding Society (UKHLS), developed a way to capture these experiences. From April 2020, participants from this Study were asked to take part in the Understanding Society COVID-19 survey, henceforth referred to as the COVID-19 survey or the COVID-19 study.

The COVID-19 survey regularly asked people about their situation and experiences. The resulting data gives a unique insight into the impact of the pandemic on individuals, families, and communities. The COVID-19 Teaching Dataset contains data from the main COVID-19 survey in a simplified form. It covers topics such as

• Socio-demographics
• Whether working at home and home-schooling
• COVID symptoms
• Health and well-being
• Social contact and neighbourhood cohesion
• Volunteering

The resource contains two data files:

• Cross-sectional: contains data collected in Wave 4 in July 2020 (with some additional variables from other waves);
• Longitudinal: Contains mainly data from Waves 1, 4 and 9 with key variables measured at three time points.

Key features of the dataset

• Missing values: in the web survey, participants clicking "Next" but not answering a question were given further options such as "Don't know" and "Prefer not to say". Missing observations like these are recorded using negative values such as -1 for "Don't know". In many instances, users of the data will need to set these values as missing. The User Guide includes Stata and SPSS code for setting negative missing values to system missing.

• The Longitudinal file is a balanced panel and is in wide format. A balanced panel means it only includes participants that took part in every wave. In wide format, each participant has one row of information, and each measurement of the same variable is a different variable.

• Weights: both the cross-sectional and longitudinal files include survey weights that adjust the sample to represent the UK adult population. The cross-sectional weight (betaindin_xw) adjusts for unequal selection probabilities in the sample design and for non-response. The longitudinal weight (ci_betaindin_lw) adjusts for the sample design and also for the fact that not all those invited to participate in the survey, do participate in all waves.

• Both the cross-sectional and longitudinal datasets include the survey design variables (psu and strata).

A full list of variables in both files can be found in the User Guide appendix.

Who is in the sample?

All adults (16 years old and over as of April 2020), in households who had participated in at least one of the last two waves of the main study Understanding Society, were invited to participate in this survey. From the September 2020 (Wave 5) survey onwards, only sample members who had completed at least one partial interview in any of the first four web surveys were invited to participate. From the November 2020 (Wave 6) survey onwards, those who had only completed the initial survey in April 2020 and none since, were no longer invited to participate

The User guide accompanying the data adds to the information here and includes a full variable list with details of measurement levels and links to the relevant questionnaire.

NOTE: THIS LAYER HAS BEEN DEPRECATED (last updated 5/31/2022). This was formerly a weekly update. Summary The number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Description MD COVID-19 - Contact Tracing Cases Reported Employment layer reflects the number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Respondents may indicate more than one category of employment if they have multiple jobs. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview. Information about how to prevent and reduce COVID-19 transmission in businesses and workplaces — including for both employers and employees — is available from the Centers for Disease Control and Prevention. Note the following regarding select employment categories: Childcare/Education: Includes teachers, babysitters, school administrators, etc. Commercial Construction and Manufacturing: Includes poultry/meat processors, electricians, carpenters, HVAC workers, welders, contractors, painters Healthcare: Includes home healthcare and administrative positions in a healthcare setting Restaurant/Food Service: Includes cooks, waitstaff, food delivery personnel, alcohol delivery services, etc. Retail, Essential Worker: Includes grocery and pharmacy workers Retail, Other: Includes all retail establishments that do not sell food or medicine Transportation: Includes positions related to transport of people or goods Other, Non-Public-Facing: Includes workers that do not have direct interactions with the public, including warehouse workers, some office workers, some car mechanics, etc. Other, Public-Facing: Includes workers who have direct interactions with the public such as, but not limited to, administrative/front desk workers, home repair workers, lawncare workers, security guards, etc. Unknown: Indicates that the interviewer was unable to ascertain the employment category based on the information provided. Answers to interview questions do not provide strong evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly how and when a case became infected. Though a person may report employment at a particular location, that does not necessarily imply that transmission happened at that location. The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Updated @ March 13, 2020

ver 0.0.12

• added additional data since last update

ver 0.0.11

• added Lat / Lon / Country Code / Region / Country Flag (image URL)
• cleaned timestamp format

Context

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. (source: CDC)

In this dataset, you will have minutes-level timesereis 2019-nCoV reporting data which can help capture the outbreak trend more accurately than the daily data.

Content

• Available File Format

  • CSV

• Time Window

  • ~0.5 Hour (may have some gaps in early mornings)

• Date Range

  • 2020-01-22 ~ 2020-03-11 (actively updating)

• Geographic Region

  • The Greater China Area (China Mainland, Hong Kong, Macau, and Taiwan)
  • The worldwide impacted areas

• Columns

  • province: String, the reported provinces / areas (not listed if no cases reported).
  • country: the country name.
  • latitude: the latitude data of the country.
  • longitude : the longitude data of the country.
  • confirmed_cases: Int, the number of confirmed cases of the place at the reporting time.
  • deaths: Int, the number of deaths of the place at the reporting time.
  • recovered, Int, the number of recovered patients at the reporting time.
  • update_time: Timestamp (CST timezone), the reporting timestamp.
  • data_source: String, the raw data sources (currently bno and dxy).
  • country_code: String, this is the country code.
  • region: String, this is the region (Europe, Asia etc.).
  • country_flag: String, this is the URL for country flag image.

Acknowledgements

Special thanks to @globalcitizen who has scrapped the raw data files from multiple public sources.

Repo here ==> https://github.com/globalcitizen/2019-wuhan-coronavirus-data

• Original Data Sources:
  • DXY.cn(Chinese medical information site)
  • bnownews.com
  • JHU

Please contact me if you consider this dataset violate your copyright and I'm happy to remove it.

Inspiration

• To the whole Kaggle community:
  • From this provided dataset, how do you see the outbreak trend of 2019-nCoV different from the historical coronavirus outbreaks (e.g. SARS, MERS)?
  • What additional dataset do you require so you can get better insights about 2019-nCov?

UPVOTES ==> Let more people know this dataset and use it to gather insights.

Appreciate it Thanks

China recorded 99256991 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, China reported 5226 Coronavirus Deaths. This dataset includes a chart with historical data for China Coronavirus Cases.

SummaryThe number of cases interviewed who had a completed answer to the question asking if they attended any gatherings of more than 10 people in the 14 days before they became ill (or had a positive test) during their covidLINK interviews.DescriptionMD COVID-19 - Contact Tracing Cases Social Gatherings of More than 10 People layer reflects the number of cases interviewed who had a completed answer to the question asking if they attended any gatherings of more than 10 people in the 14 days before they became ill (or had a positive test) during their covidLINK interviews. Respondents may indicate that they attended more than one category of social gathering. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview.Events and locations where there is prolonged exposure to other people — including weddings, parties, stores, restaurants, etc. — are considered “high risk” for COVID-19 transmission. The more interaction at a gathering or location, the more likely a person may be to transmit or become infected with the virus. More information about considerations for events and gatherings — including how to assess risk levels and promote healthy behaviors that reduce spread — is available from the Centers for Disease Control and Prevention.Answers to interview questions do not provide evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly where and when a case became infected. Though a person may report attendance at a particular location, that does not mean that transmission happened at that location.The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

The Covid-19 pandemic has completely reshaped the lives of people around the world, including the lives of higher education students. Beyond serious health consequences for a proportion of those directly affected by the virus, the pandemic holds important implications for the life and work of higher education students, considerably affecting their physical and mental well-being. To capture how students perceived the first wave of the pandemic’s impact, one of the most comprehensive and large-scale online surveys across the world was conducted. Carried out between 5 May 2020 and 15 June 2020, the survey came at a time when most countries were experiencing the arduous lockdown restrictions. The online questionnaire was prepared in seven different languages (English, Italian, North Macedonian, Portuguese, Romanian, Spanish, Turkish) and covered various aspects of higher education students’ life, including socio-demographic and academic characteristics, academic life, infrastructure and skills for studying from home, social life, emotional life and life circumstances. Using the convenience sampling method, the online questionnaire was distributed to higher education students (aged 18 and over) and enrolled in a higher education institution. The final dataset consisted of 31,212 responses from 133 countries and 6 continents. The data may prove useful for researchers studying the pandemic’s impacts on various aspects of student life. Policymakers can utilize the data to determine the best solutions as they formulate policy recommendations and strategies to support students during this and any future pandemic.

THE USE OF DRUGS AND GETTING CAUGHT IS AN OLD ONE. Catch this new dataset to get some knowledge of the world's greatest drug seizues. The dataset has 7 columns all categorized by their region. Try dataset to salute the world's various drug enforcement departments.

The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). Additionally, the NHCS contributes data that may inform public health emergencies as the survey is designed to capture emerging diseases and viruses that require hospitalizations, including COVID-19 encounters. The 2020 - 2023 NHCS are not yet fully operational so it is important to note that these data are not nationally representative.

The data are from 26 hospitals submitting inpatient and 26 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from March 18, 2020-December 26, 2023. Even though the data are not nationally representative, they can provide insight on the impact of COVID-19 on various types of hospitals throughout the country. This information is not available in other hospital reporting systems. The NHCS data from these hospitals can show results by a combination of indicators related to COVID-19, such as length of inpatient stay, in-hospital mortality, comorbidities, and intubation or ventilator use. NHCS data allow for reporting on patient conditions and treatments within the hospital over time.

Extracellular vesicles (EVs) released by virus-infected cells have the potential to encapsulate viral peptides, a characteristic that could facilitate vaccine development. Furthermore, plasma-derived EVs may elucidate pathological changes occurring in distal tissues during viral infections. We hypothesized that molecular characterization of EVs isolated from COVID-19 patients would reveal peptides suitable for vaccine development. Blood samples were collected from three cohorts: severe COVID-19 patients (G1), mild/asymptomatic cases (G2), and SARS-CoV-2-negative healthcare workers (G3). Samples were obtained at two time points: during the initial phase of the pandemic in early 2020 (m0) and eight months later (m8). Clinical data analysis revealed elevated inflammatory markers in G1. Notably, non-vaccinated individuals in G1 exhibited increased levels of neutralizing antibodies at m8, suggesting prolonged exposure to viral antigens. Proteomic profiling of EVs was performed using three distinct methods: immunocapture (targeting CD9), ganglioside-capture (utilizing Siglec-1) and size-exclusion chromatography (SEC). Contrary to our hypothesis, this analysis failed to identify viral peptides. These findings were subsequently validated through Western blot analysis targeting the RBD of the SARS-CoV-2 Spike protein’s and comparative studies using samples from experimentally infected Syrian hamsters. Furthermore, analysis of the EV cargo revealed a diverse molecular profile, including components involved in the regulation of viral replication, systemic inflammation, antigen presentation, and stress responses. These findings underscore the potential significance of EVs in the pathogenesis and progression of COVID-19.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991. The Understanding Society: Calendar Year Dataset, 2021, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2021, and, therefore, combine data collected in three waves (Waves 11, 12 and 13). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2021 dataset is the second of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2022 are included), data structure and additional supporting information can be found in the document '9193_calendar_year_dataset_2021_user_guide'. As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move, they are followed within the UK, and anyone joining their households is also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7) and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey has been conducted by web and telephone only but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020, an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset. Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage. Co-funders In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency. End User Licence and Special Licence versions: There are two versions of the Calendar Year 2021 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see the document '9194_eul_vs_sl_variable_differences' for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2021, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2021, and, therefore, combine data collected in three waves (Waves 11, 12 and 13). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2021 dataset is the second of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2022 are included), data structure and additional supporting information can be found in the document '9194_calendar_year_dataset_2020_user_guide'.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move, they are followed within the UK, and anyone joining their households is also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7) and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended, and the survey has been conducted by web and telephone only but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020, an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders

In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:

There are two versions of the Calendar Year 2021 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2021 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software

These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

SummaryThe number of cases interviewed who had a completed answer to the question asking if they visited or worked at any of a list of high risk locations in the 14 days before they became ill (or had a positive test) during their covidLINK interviews.DescriptionMD COVID-19 - Contact Tracing Cases High Risk Locations layer reflects the number of cases interviewed who had a completed answer to the question asking if they visited or worked at any of a list of high risk locations in the 14 days before they became ill (or had a positive test) during their covidLINK interviews. Respondents may indicate that they visited or worked at more than one category of high risk location. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview.Events and locations where there is prolonged exposure to other people — including weddings, parties, stores, restaurants, etc. — are considered “high risk” for COVID-19 transmission. The more interaction at a gathering or location, the more likely a person may be to transmit or become infected with the virus. More information about considerations for events and gatherings — including how to assess risk levels and promote healthy behaviors that reduce spread — is available from the Centers for Disease Control and Prevention.Answers to interview questions do not provide evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly where and when a case became infected. Though a person may report attendance at a particular location, that does not mean that transmission happened at that location.The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

SummaryThe number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews.DescriptionMD COVID-19 - Contact Tracing Cases Reported Employment layer reflects the number of cases interviewed who had a completed answer to the question asking if they had physically gone to work in the last 14 days during their covidLINK interviews. Respondents may indicate more than one category of employment if they have multiple jobs. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview. Information about how to prevent and reduce COVID-19 transmission in businesses and workplaces — including for both employers and employees — is available from the Centers for Disease Control and Prevention.Note the following regarding select employment categories:Childcare/Education: Includes teachers, babysitters, school administrators, etc.Commercial Construction and Manufacturing: Includes poultry/meat processors, electricians, carpenters, HVAC workers, welders, contractors, paintersHealthcare: Includes home healthcare and administrative positions in a healthcare settingRestaurant/Food Service: Includes cooks, waitstaff, food delivery personnel, alcohol delivery services, etc.Retail, Essential Worker: Includes grocery and pharmacy workersRetail, Other: Includes all retail establishments that do not sell food or medicineTransportation: Includes positions related to transport of people or goodsOther, Non-Public-Facing: Includes workers that do not have direct interactions with the public, including warehouse workers, some office workers, some car mechanics, etc.Other, Public-Facing: Includes workers who have direct interactions with the public such as, but not limited to, administrative/front desk workers, home repair workers, lawncare workers, security guards, etc.Unknown: Indicates that the interviewer was unable to ascertain the employment category based on the information provided.Answers to interview questions do not provide strong evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly how and when a case became infected. Though a person may report employment at a particular location, that does not necessarily imply that transmission happened at that location.The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society: Calendar Year Dataset, 2020, is designed to enable cross-sectional analysis of individuals and households relating specifically to their annual interviews conducted in the year 2020, and, therefore, combine data collected in three waves (Waves 10, 11 and 12). It has been produced from the same data collected in the main Understanding Society study and released in the longitudinal datasets SN 6614 (End User Licence) and SN 6931 (Special Licence). Such cross-sectional analysis can, however, only involve variables that are collected in every wave in order to have data for the full sample panel. The 2020 dataset is the first of a series of planned Calendar Year Datasets to facilitate cross-sectional analysis of specific years. Full details of the Calendar Year Dataset sample structure (including why some individual interviews from 2021 are included), data structure and additional supporting information can be found in the 8988_calendar_year_dataset_2020_user_guide.

As multi-topic studies, the purpose of Understanding Society is to understand the short- and long-term effects of social and economic change in the UK at the household and individual levels. The study has a strong emphasis on domains of family and social ties, employment, education, financial resources, and health. Understanding Society is an annual survey of each adult member of a nationally representative sample. The same individuals are re-interviewed in each wave approximately 12 months apart. When individuals move they are followed within the UK and anyone joining their households are also interviewed as long as they are living with them. The fieldwork period for a single wave is 24 months. Data collection uses computer-assisted personal interviewing (CAPI) and web interviews (from wave 7), and includes a telephone mop-up. From March 2020 (the end of wave 10 and 2nd year of wave 11), due to the coronavirus pandemic, face-to-face interviews were suspended and the survey has been conducted by web and telephone only, but otherwise has continued as before. One person completes the household questionnaire. Each person aged 16 or older participates in the individual adult interview and self-completed questionnaire. Youths aged 10 to 15 are asked to respond to a paper self-completion questionnaire. In 2020 an additional frequent web survey was separately issued to sample members to capture data on the rapid changes in people’s lives due to the COVID-19 pandemic (see SN 8644). The COVID-19 Survey data are not included in this dataset.

Further information may be found on the "https://www.understandingsociety.ac.uk/documentation/mainstage"> Understanding Society main stage webpage and links to publications based on the study can be found on the Understanding Society Latest Research webpage.

Co-funders
In addition to the Economic and Social Research Council, co-funders for the study included the Department of Work and Pensions, the Department for Education, the Department for Transport, the Department of Culture, Media and Sport, the Department for Community and Local Government, the Department of Health, the Scottish Government, the Welsh Assembly Government, the Northern Ireland Executive, the Department of Environment and Rural Affairs, and the Food Standards Agency.

End User Licence and Special Licence versions:
There are two versions of the Calendar Year 2020 data. One is available under the standard End User Licence (EUL) agreement, and the other is a Special Licence (SL) version. The SL version contains month and year of birth variables instead of just age, more detailed country and occupation coding for a number of variables and various income variables have not been top-coded (see xxxx_eul_vs_sl_variable_differences for more details). Users are advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. The SL data have more restrictive access conditions; prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. The main longitudinal versions of the Understanding Society study may be found under SNs 6614 (EUL) and 6931 (SL).

Low- and Medium-level geographical identifiers produced for the mainstage longitudinal dataset can be used with this Calendar Year 2020 dataset, subject to SL access conditions. See the User Guide for further details.

Suitable data analysis software
These data are provided by the depositor in Stata format. Users are strongly advised to analyse them in Stata. Transfer to other formats may result in unforeseen issues. Stata SE or MP software is needed to analyse the larger files, which contain about 1,900 variables.

BackgroundGenetic factors contribute to individual differences in the severity of coronavirus disease 2019 (COVID-19). A portion of genetic predisposition can be captured using polygenic risk scores (PRS). Relatively little is known about the associations between PRS and COVID-19 severity or post-acute COVID-19 in community-dwelling individuals.MethodsParticipants in this study were 983 World Trade Center responders infected for the first time with SARS-CoV-2 (mean age at infection = 56.06; 93.4% male; 82.7% European ancestry). Seventy-five (7.6%) responders were in the severe COVID-19 category; 306 (31.1%) reported at least one post-acute COVID-19 symptom at 4-week follow-up. Analyses were adjusted for population stratification and demographic covariates.FindingsThe asthma PRS was associated with severe COVID-19 category (odds ratio [OR] = 1.61, 95% confidence interval: 1.17–2.21) and more severe COVID-19 symptomatology (β = .09, p = .01), independently of respiratory disease diagnosis. Severe COVID-19 category was also associated with the allergic disease PRS (OR = 1.97, [1.26–3.07]) and the PRS for COVID-19 hospitalization (OR = 1.35, [1.01–1.82]). PRS for coronary artery disease and type II diabetes were not associated with COVID-19 severity.ConclusionRecently developed polygenic biomarkers for asthma, allergic disease, and COVID-19 hospitalization capture some of the individual differences in severity and clinical course of COVID-19 illness in a community population.

On July 26, 2022, the amount of positive coronavirus (COVID-19) cases increased by 425 in Nigeria, reaching 260,764 cases in total. As of the same date, there were about 3.14 thousand casualties and over 250 thousand recoveries in the country. Nigeria is the eleventh highest African country in terms of registered cases. December 22, 2021 recorded the highest daily increase in cases in Nigeria since the beginning of the pandemic.