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)

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.

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.

If a model utilized data from multiple categories, it was placed in each.

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.

Epidemiological databases (and data) used for analyses of radiation workers and nuclear weapons test participants.

IQR = interquartile range; SCC = Swiss Concussion Center.

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.

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.

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.

1The totals in this column equal the number of articles reporting a particular type of funding, minus instances of duplicate classification by type of company within funding category. These instances were: There was no information on funding for the article classified as both manufacturing and mining, and non-profit, non-governmental funding was used by the articles classified as both tobacco and transportation and both tobacco and alcohol. The overall column total is greater than the total number of included articles (N = 361) because some articles reported multiple types of funding.2Other funding sources include Blue Cross Blue Shield (4 tobacco articles), the World Health Organization (2 tobacco articles), and funding from a law firm (1 manufacturing article).3The totals in this row equal the total number of articles reporting funding for each type of company, minus instances where articles reported multiple types of funding, of which there are too many to list. The totals for the columns are therefore not equal to the sum of the classifications within the columns. The overall row total is greater than the total number of included articles (N = 361) because three articles were classified with two types of companies.

Epidemiology Analytics AI Market Outlook

According to our latest research, the global Epidemiology Analytics AI market size reached USD 2.8 billion in 2024, reflecting the rapidly growing adoption of artificial intelligence in public health and epidemiological research. The market is anticipated to expand at a robust CAGR of 22.4% from 2025 to 2033, which will propel the market to an estimated USD 21.1 billion by 2033. This exponential growth is primarily driven by the urgent need for advanced data analytics to manage infectious diseases, optimize healthcare resources, and support real-time decision-making in public health. As per the latest research, the increasing prevalence of emerging diseases and the global shift toward digital healthcare infrastructure are acting as significant catalysts for the market’s expansion.

One of the primary growth factors for the Epidemiology Analytics AI market is the mounting pressure on global healthcare systems to respond swiftly to disease outbreaks and pandemics. The COVID-19 pandemic highlighted the critical need for real-time analytics and predictive modeling to track infection rates, allocate resources, and inform policy decisions. AI-powered epidemiology analytics solutions offer unparalleled capabilities in processing vast datasets, identifying patterns, and providing actionable insights for disease surveillance and outbreak prediction. This has led to widespread investments by governments, healthcare providers, and research institutions in AI-driven epidemiological tools, further fueling market growth.

Another significant driver is the rapid advancement of AI technologies, including machine learning, natural language processing, and advanced data visualization. These technologies enable more accurate risk assessment, early detection of disease clusters, and improved clinical research outcomes. The integration of AI with existing epidemiological databases and electronic health records (EHRs) has enhanced the ability to analyze patient-level data, track disease progression, and forecast healthcare demands. Additionally, the proliferation of wearable devices and IoT-enabled health monitoring systems has generated massive volumes of real-time data, which can be leveraged by AI analytics platforms to deliver more precise epidemiological insights.

The increasing adoption of cloud-based solutions and the expansion of digital health infrastructure across both developed and emerging markets are also propelling the growth of the Epidemiology Analytics AI market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling organizations to access powerful analytics tools without significant upfront investments in hardware. This democratization of access to advanced analytics is particularly beneficial for low- and middle-income countries, where resource constraints have traditionally limited the adoption of sophisticated epidemiological tools. The convergence of AI, big data, and cloud computing is thus creating new opportunities for innovation and market penetration.

Regionally, North America holds the largest share of the Epidemiology Analytics AI market, driven by substantial investments in healthcare IT, a strong presence of leading technology providers, and robust government initiatives in disease surveillance. Europe follows closely, benefiting from collaborative research projects and stringent regulatory frameworks that promote data interoperability and patient safety. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rising healthcare expenditures, increasing digitalization, and a growing burden of infectious and chronic diseases. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI analytics, primarily through pilot projects and international collaborations, setting the stage for future market expansion.

Component Analysis

The Epidemiology Analytics AI market by component is segmented into software, hardware, and services, each playing a pivotal role in the overall ecosystem. Software solutions dominate the market, accounting for the largest revenue share in 2024. These include AI-powered analytics platforms, data visualization tools, and predictive modeling applications that enable real-time disease surveillance and outbreak forecasting. The continuous evolution of software capabilities, such as integration with electronic health records (EHRs) and intero

Epidemiological data sources.

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).

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

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.

Epidemiological data on study population.