CPSC's epidemiological data include reports of incidents involving death, injury, or potential injury that are associated with consumer products. The online Clearinghouse posts summary information from death certificates (DTHS), medical examiner reports (MECAP reports), reports published on Saferproducts.gov, Newsclips, and other submissions from consumers, healthcare professionals, state, federal, and local agencies (IPII), and public safety entities.

HLA+  =  carrier of ≥ 1 copy of the DRB1*1501 allele*From Canadian Data [21], based on a prevalence of 150 per 105 population and split into men and women according to [15]. Concordance rates presented as “proband-wise” rates [30].†Data unavailable on the 2 male patients [21]. The worst case is: 9/11  =  0.82**See: Prop. (1.4) of Appendix S1 (Section C)##Canadian HLA data: D Sadovnick (personal communication). Based on ∼ 3,000 cases and ∼ 400 Controls (% women not available). Control rates confirmed in a much larger transplant database.††UCSF Databases: J Oksenberg (personal communication) UCSF #1 (GeneMSA) - 485 cases (68% women) and 431 Controls (66% women) UCSF #2 (IMSGC) - 779 cases (76% women)

Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

Data and code repository for the Open COVID-19 Data Working Group: a global and multi-organizational initative that aims to enable rapid sharing of trusted and open public health data to advance the response to infectious diseases.

Context

The dataset contains data of documented COVID-19 cases, partially complemented by additional Web data. The date are originally pulished at https://tinyurl.com/s6gsq5y and are updated regularly. This dataset is a snapshot downloaded on March 3, 2020.

Content

Xu et al. (2020) have built a centralised repository of individual-level information on patients with laboratory-confirmed COVID-19 (in China, confirmed by detection of virus nucleic acid at the City and Provincial Centers for Disease Control and Prevention), including their travel history, location (highest resolution available and corresponding latitude and longitude), symptoms, and reported onset dates, as well as confirmation dates and basic demographics. Information is collated from a variety of sources, including official reports from WHO, Ministries of Health, and Chinese local, provincial, and national health authorities. If additional data are available from reliable online reports, they are included.

Acknowledgements

The authors decidedly declare no competing interests. Their work was funded by the Oxford Martin School.

Reference

Xu, Bo; Kraemer, Moritz U. G.; Gutierrez, Bernardo; Mekaru, Sumiko; Sewalk, Kara; Loskill, Alyssa et al. (2020): Open access epidemiological data from the COVID-19 outbreak. In: The Lancet Infectious Diseases. DOI: 10.1016/S1473-3099(20)30119-5.

RS: Retrospective study.

This database includes confirmed cases of COVID-19 in line list format. It spans the timeframe between 1 December 2019 to 5 February 2020.

An ongoing outbreak of the viral disease monkeypox was confirmed in May 2022, beginning with a cluster of cases found in the United Kingdom. The first confirmed case was traced to an individual with travel links to Nigeria and was detected on 6 May 2022. During the early stages of outbreaks, obtaining reliable, synthesised data on the characteristics of cases is a challenge, especially at a global scale.

IQR = interquartile range; SCC = Swiss Concussion Center.

WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

The dataset contains a weekly situation update on COVID-19, the epidemiological curve and the global geographical distribution (EU/EEA and the UK, worldwide).

Since the beginning of the coronavirus pandemic, ECDC’s Epidemic Intelligence team has collected the number of COVID-19 cases and deaths, based on reports from health authorities worldwide. This comprehensive and systematic process was carried out on a daily basis until 14/12/2020. See the discontinued daily dataset: COVID-19 Coronavirus data - daily. ECDC’s decision to discontinue daily data collection is based on the fact that the daily number of cases reported or published by countries is frequently subject to retrospective corrections, delays in reporting and/or clustered reporting of data for several days. Therefore, the daily number of cases may not reflect the true number of cases at EU/EEA level at a given day of reporting. Consequently, day to day variations in the number of cases does not constitute a valid basis for policy decisions.

ECDC continues to monitor the situation. Every week between Monday and Wednesday, a team of epidemiologists screen up to 500 relevant sources to collect the latest figures for publication on Thursday. The data screening is followed by ECDC’s standard epidemic intelligence process for which every single data entry is validated and documented in an ECDC database. An extract of this database, complete with up-to-date figures and data visualisations, is then shared on the ECDC website, ensuring a maximum level of transparency.

ECDC receives regular updates from EU/EEA countries through the Early Warning and Response System (EWRS), The European Surveillance System (TESSy), the World Health Organization (WHO) and email exchanges with other international stakeholders. This information is complemented by screening up to 500 sources every day to collect COVID-19 figures from 196 countries. This includes websites of ministries of health (43% of the total number of sources), websites of public health institutes (9%), websites from other national authorities (ministries of social services and welfare, governments, prime minister cabinets, cabinets of ministries, websites on health statistics and official response teams) (6%), WHO websites and WHO situation reports (2%), and official dashboards and interactive maps from national and international institutions (10%). In addition, ECDC screens social media accounts maintained by national authorities on for example Twitter, Facebook, YouTube or Telegram accounts run by ministries of health (28%) and other official sources (e.g. official media outlets) (2%). Several media and social media sources are screened to gather additional information which can be validated with the official sources previously mentioned. Only cases and deaths reported by the national and regional competent authorities from the countries and territories listed are aggregated in our database.

Disclaimer: National updates are published at different times and in different time zones. This, and the time ECDC needs to process these data, might lead to discrepancies between the national numbers and the numbers published by ECDC. Users are advised to use all data with caution and awareness of their limitations. Data are subject to retrospective corrections; corrected datasets are released as soon as processing of updated national data has been completed.

If you reuse or enrich this dataset, please share it with us.

A 20 year, 20,000 person, open longitudinal epidemiological study of a cohort town. GAZEL was not constructed to answer a specific question rather it was designed to help analyze a wide range of scientific problems and is accessible to the community of researchers specializing in epidemiology. Translation is not available for all pages. The GAZEL cohort, set up in 1989 by Inserm Unit 88 (subsequently Unit 687), in cooperation with several departments of ��lectricit�� de France-Gaz de France (EDF-GDF), was a public utility firm in France involved in production, transmission and distribution of energy. GAZEL initially included 20 624 volunteers working at EDF-GDF (15 010 men and 5614 women), aged from 35 to 50 years. In accordance with its purpose as a scientific research platform, the GAZEL cohort is permanently open to epidemiologic research teams. Today, more than 50 projects on very diversified themes have been set up in GAZEL by some 20 teams, French, belonging to different bodies, and foreign (Germany, Belgium, Canada, Great Britain, Sweden, Finland, and USA).

The 2017 nucleic acid and amino acid sequence data for enteroviruses and influenza viruses, as well as corresponding epidemiological data. It can be presented by the counties and cities within the country. The sequence data can facilitate further research by domestic and external researchers.

This dataset contains two files: (i) An annotated corpus ("epi_info_corpus‧xlsx") containing 486 manually annotated sentences extracted from 32 animal disease-related news articles. These news articles were obtained from the database of an event-based biosurveillance system dedicated to animal health surveillance, PADI-web (https://padi-web.cirad.fr/en/). The first sheet (‘article_metadata’) provides metadata about the news articles : (1) id_article, the unique id of a news article, (2) title, the title of the news article, (3) source, the name of the news article website, (3) publication_date, the publication date of the news article (mm-dd-yyyy) and (4) URL, the web URL of the news article. The second sheet (‘annot_sentences’) contains the annotated sentences: each row corresponds to a sentence from a news article. Each sentence has two distinct labels, Event type and Information type. The set of columns is : (1) id_article, the id of the news article to which the sentence belongs, (2) id_sentence, the unique id of the sentence, indicating its position in the news content (integer ranging from 1 to n, n being the total number of sentences in the news article), (3) sentence_text, the sentence textual content, (4) event_type, the Event type label and (5) information_type, the Information type label. Event type labels indicate the relation between the sentence and the epidemiological context, i‧e. current event (CE), risk event (RE), old event (OE), general (G) and irrelevant (IR). Information type labels indicate the type of epidemiological information, i‧e descriptive epidemiology (DE), distribution (DI), preventive and control measures (PCM), economic and political consequences (EPC), transmission pathway (TP), concern and risk factors (CRF), general epidemiology (GE) and irrelevant (IR). (ii) The annotation guidelines ("epi_info_guidelines‧doc") providing a detailed description of each category.

Demographic and epidemiological data of participants at the time of enrollment.

A full anonymized data set collected as a part of ICU infection control and surveillance program; 01/01/2011-01/01-2018

Files "VAE_Data_Main_0821_1338.csv" contains daily data (one row is one day) on infection surveillance ordered by date.

Files "Data_Dictionary_MainDB.csv" contains the description of all variables from the main data set.

This dataset contains key characteristics about the data described in the Data Descriptor Epidemiological data from the COVID-19 outbreak, real-time case information. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format

This interactive tool allows users to generate tables and graphs on information relating to pregnancy and childbirth. All data comes from the CDC's PRAMS. Topics include: breastfeeding, prenatal care, insurance coverage and alcohol use during pregnancy. Background CPONDER is the interaction online data tool for the Center's for Disease Control and Prevention (CDC)'s Pregnancy Risk Assessment Monitoring System (PRAMS). PRAMS gathers state and national level data on a variety of topics related to pregnancy and childbirth. Examples of information include: breastfeeding, alcohol use, multivitamin use, prenatal care, and contraception. User Functionality Users select choices from three drop down menus to search for d ata. The menus are state, year and topic. Users can then select the specific question from PRAMS they are interested in, and the data table or graph will appear. Users can then compare that question to another state or to another year to generate a new data table or graph. Data Notes The data source for CPONDER is PRAMS. The data is from every year between 2000 and 2008, and data is available at the state and national level. However, states must have participated in PRAMS to be part of CPONDER. Not every state, and not every year for every state, is available.

COVID-19 integrated surveillance data provided by the Italian Institute of Health and processed via UnrollingAverages.jl to deconvolve the weekly moving averages.