33 datasets found
  1. A

    Provisional Death Counts for Influenza, Pneumonia, and COVID-19

    • data.amerigeoss.org
    • data.virginia.gov
    • +4more
    csv, json, rdf, xml
    Updated Aug 4, 2022
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    United States (2022). Provisional Death Counts for Influenza, Pneumonia, and COVID-19 [Dataset]. https://data.amerigeoss.org/es/dataset/provisional-death-counts-for-influenza-pneumonia-and-covid-19-d8b6f
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    json, xml, csv, rdfAvailable download formats
    Dataset updated
    Aug 4, 2022
    Dataset provided by
    United States
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    Deaths counts for influenza, pneumonia, and coronavirus disease 2019 (COVID-19) reported to NCHS by week ending date, by state and HHS region, and age group.

  2. A

    ‘NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections’ analyzed by...

    • analyst-2.ai
    Updated Oct 20, 2018
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2018). ‘NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-nndss-table-1y-mumps-to-novel-influenza-a-virus-infections-6e03/latest
    Explore at:
    Dataset updated
    Oct 20, 2018
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/dbaa3ee8-2cb2-4510-b8e6-1ba0c435c1de on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

    Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

    Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published — CDC does not have data because of changes in how conditions are categorized. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2018 and 2019 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the US, a US territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-US Residents' category. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. † Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). § Novel influenza A virus infections are human infections with influenza A viruses that are different from currently circulating human seasonal influenza viruses. With the exception of one avian lineage influenza A (H7N2) virus, all novel influenza A virus infections reported to CDC since 2012 have been variant influenza viruses.

    --- Original source retains full ownership of the source dataset ---

  3. NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated...

    • data.virginia.gov
    • healthdata.gov
    • +4more
    csv, json, rdf, xsl
    Updated Apr 26, 2019
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    Centers for Disease Control and Prevention (2019). NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated pediatric mortality [Dataset]. https://data.virginia.gov/dataset/nndss-table-1r-hepatitis-c-perinatal-infection-to-influenza-associated-pediatric-mortality
    Explore at:
    json, xsl, csv, rdfAvailable download formats
    Dataset updated
    Apr 26, 2019
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated pediatric mortality - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

    Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

    Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published — CDC does not have data because of changes in how conditions are categorized. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2018 and 2019 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the US, a US territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-US Residents' category. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. † Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). § Since [INSERT DATE], XXX influenza-associated pediatric deaths occurring during the 2017-18 season have been reported.

  4. s

    Dataset of SARS-CoV-2, influenza A and influenza B virus content in...

    • figshare.scilifelab.se
    txt
    Updated Jan 15, 2025
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    Anna Székely; Maja Malmberg; Javier Vargas; Nahla Mohamed; Israa Dafalla; Filip Petrini; Lauren Davies (2025). Dataset of SARS-CoV-2, influenza A and influenza B virus content in wastewater samples from wastewater treatment plants in Sweden [Dataset]. http://doi.org/10.17044/scilifelab.14256317.v2
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    txtAvailable download formats
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    Swedish University of Agricultural Sciences
    Authors
    Anna Székely; Maja Malmberg; Javier Vargas; Nahla Mohamed; Israa Dafalla; Filip Petrini; Lauren Davies
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sweden
    Description

    This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala. The dataset is part of a research study led by associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program. The data is generated by weekly SARS-CoV-2, influenza A and influenza B virus measurements in wastewater samples from Sweden. The monitoring started in Uppsala in August 2020, while other places joined the program later. For all places, raw, untreated wastewater samples representative of a single day (24 hours) are collected by flow compensated samplers. All measurements represent only 1 day except for Uppsala, where since week 16, 2021 the measurements represent 1 week as samples are collected each day and then combined flow-proportionally into 1 composite weekly sample. The samples are processed according to standard methods. Briefly, the viral genomic material is concentrated and extracted by the direct capture method using the Maxwell RSC Enviro TNA kit (Promega) and the copy number of SARS-CoV-2 genomes is quantified by RT-qPCR using the CDC RUO nCOV N1 assay (IDT DNA). To correct for variations in population size and wastewater flow, we also quantify the pepper mild mottle virus (PMMoV) which is the most abundant RNA virus in human feces and serves as an estimator of human fecal content (Symonds et al., 2019). For more about the evaluation of this normalization method, please consult the corresponding publication: Isaksson et al. (2022). The data is presented on the graph as the ratio of the copy numbers measured by the N1 and PMMoV-assays multiplied by 10^4. As N1 copy number is a proxy for SARS-CoV-2 virus content in the wastewater and PMMoV is a proxy of the fecal content, which is related to the contributing population, this ratio can be considered as proxy of the prevalence of infections in the population of the wastewater catchment area. The dataset is available as part of the Environmental Virus Profiling data section "The amount of SARS-CoV-2 virus in wastewater across Sweden". It can be found here and downloaded under the heading "Dataset". Note that the dataset is preliminary. The team is still conducting method efficiency checks that might slightly affect the final results.

  5. d

    Data from: Cost‑effectiveness analysis of anti‑influenza drugs in adult...

    • datadryad.org
    • data.niaid.nih.gov
    zip
    Updated Nov 17, 2023
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    Naoto Nakagawa (2023). Cost‑effectiveness analysis of anti‑influenza drugs in adult outpatient settings in Japan [Dataset]. http://doi.org/10.5061/dryad.m905qfv4n
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    zipAvailable download formats
    Dataset updated
    Nov 17, 2023
    Dataset provided by
    Dryad
    Authors
    Naoto Nakagawa
    Time period covered
    2022
    Area covered
    Japan
    Description

    Cost-effectiveness analysis of anti-influenza drugs in adult outpatient settings in Japan

    File list

    QOL scores.csv QALY.csv

    File descriptions

    Details for: QOL scores The file is the table of raw data using EQ-5D-5L questionnaire. Format(s): .csv Dimensions: 50 rows x 8 columns

    Variables:

    • Patient No: anonymous number of each patient.
    • Disease state 1: QOL score of disease state 1 based on the EQ-5D-5L.
    • Disease state 2: QOL score of disease state 2 based on the EQ-5D-5L.
    • Disease state 3: QOL score of disease state 3 based on the EQ-5D-5L.
    • Disease state 4: QOL score of disease state 4 based on the EQ-5D-5L.
    • Disease state 5: QOL score of disease state 5 based on the EQ-5D-5L.
    • Disease state 6: QOL score of disease state 6 based on the EQ-5D-5L.
    • Disease state 7: QOL score of disease state 7 based on the EQ-5D-5L.

    Details for: QALY.csv The file is the table to be converted each QOL score using the formula in the paper. Format(s): .csv Dimensions: 50 rows x 8 co...

  6. NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections

    • data.cdc.gov
    • healthdata.gov
    • +2more
    application/rdfxml +5
    Updated Jan 2, 2020
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    Division of Health Informatics and Surveillance (DHIS), Centers for Disease Control and Prevention (2020). NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections [Dataset]. https://data.cdc.gov/NNDSS/NNDSS-TABLE-1Y-Mumps-to-Novel-influenza-A-virus-in/cjin-8pa3
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    csv, application/rdfxml, xml, tsv, application/rssxml, jsonAvailable download formats
    Dataset updated
    Jan 2, 2020
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Authors
    Division of Health Informatics and Surveillance (DHIS), Centers for Disease Control and Prevention
    Description

    NNDSS - TABLE 1Y. Mumps to Novel influenza A virus infections - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents.

    Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html.

    Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published — CDC does not have data because of changes in how conditions are categorized. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2018 and 2019 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the US, a US territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-US Residents' category. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. † Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data). § Novel influenza A virus infections are human infections with influenza A viruses that are different from currently circulating human seasonal influenza viruses. With the exception of one avian lineage influenza A (H7N2) virus, all novel influenza A virus infections reported to CDC since 2012 have been variant influenza viruses.

  7. COVID-19 Deaths in the US

    • kaggle.com
    Updated Aug 15, 2020
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    Edward Zhang (2020). COVID-19 Deaths in the US [Dataset]. https://www.kaggle.com/sshikamaru/covid19-deaths-in-the-us/activity
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 15, 2020
    Dataset provided by
    Kaggle
    Authors
    Edward Zhang
    License

    https://www.usa.gov/government-works/https://www.usa.gov/government-works/

    Area covered
    United States
    Description

    Context

    Corona virus cases in the US is stacking up higher and higher. Understanding this virus is crucial to stopping it's spread.

    Content

    The dataset shows, deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.

    Acknowledgements

    Credits to this data set comes from : https://data.cdc.gov/NCHS/Provisional-COVID-19-Death-Counts-by-Sex-Age-and-S/9bhg-hcku

  8. f

    Data values for tables and figures.

    • plos.figshare.com
    xlsx
    Updated May 22, 2025
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    Jennifer L. Matas; Kira Raskina; Sabine Tong; Derrick Forney; Bruno Scarpellini; Mario Cruz-Rivera; Gary Puckrein; Liou Xu (2025). Data values for tables and figures. [Dataset]. http://doi.org/10.1371/journal.pone.0321208.s007
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    xlsxAvailable download formats
    Dataset updated
    May 22, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Jennifer L. Matas; Kira Raskina; Sabine Tong; Derrick Forney; Bruno Scarpellini; Mario Cruz-Rivera; Gary Puckrein; Liou Xu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundInfluenza-related healthcare utilization among Medicaid patients and commercially insured patients is not well-understood. This study compared influenza-related healthcare utilization and assessed disease management among individuals diagnosed with influenza during the 2015–2019 influenza seasons.MethodsThis retrospective cohort study identified influenza cases among adults (18–64 years) using data from the Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Research Identifiable Files (RIF) and Optum’s de-identified Clinformatics® Data Mart Database (CDM). Influenza-related healthcare utilization rates were calculated per 100,000 patients by setting (outpatient, emergency department (ED), inpatient hospitalizations, and intensive care unit (ICU) admissions) and demographics (sex, race, and region). Rate ratios were computed to compare results from both databases. Influenza episode management assessment included the distribution of the index point-of-care, antiviral prescriptions, and laboratory tests obtained.ResultsThe Medicaid population had a higher representation of racial/ethnic minorities than the CDM population. In the Medicaid population, influenza-related visits in outpatient and ED settings were the most frequent forms of healthcare utilization, with similar rates of 652 and 637 visits per 100,000, respectively. In contrast, the CDM population predominantly utilized outpatient settings. Non-Hispanic Blacks and Hispanics exhibited the highest rates of influenza-related ED visits in both cohorts. In the Medicaid population, Black (64.5%) and Hispanic (51.6%) patients predominantly used the ED as their index point-of-care for influenza. Overall, a greater proportion of Medicaid beneficiaries (49.8%) did not fill any influenza antiviral prescription compared to the CDM population (37.0%).ConclusionAddressing disparities in influenza-related healthcare utilization between Medicaid and CDM populations is crucial for equitable healthcare access. Targeted interventions are needed to improve primary care and antiviral access and reduce ED reliance, especially among racial/ethnic minorities and low-income populations.

  9. R

    An annotated Avian Influenza dataset from two event-based surveillance...

    • entrepot.recherche.data.gouv.fr
    Updated May 10, 2023
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    El Bahdja Boudoua; El Bahdja Boudoua (2023). An annotated Avian Influenza dataset from two event-based surveillance systems [Dataset]. http://doi.org/10.57745/6R81RT
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    text/comma-separated-values(98348), text/comma-separated-values(56800)Available download formats
    Dataset updated
    May 10, 2023
    Dataset provided by
    Recherche Data Gouv
    Authors
    El Bahdja Boudoua; El Bahdja Boudoua
    License

    https://spdx.org/licenses/etalab-2.0.htmlhttps://spdx.org/licenses/etalab-2.0.html

    Description

    These dataset concerns Avian Influenza (AI) data from news items (articles) collected by two event-based surveillance systems; HealthMap and PADI-Web published between 2018 and 2019. Collected articles were manually annotated by relevance for epidemic intelligence purposes. A relevant article reports one epiemiological avian influenza event (outbreak) or more while, an irrelevant article is related to sanitary/political/economic measures or mentions another disease. This dataset can be used to train or evaluate classification approaches and classify these AI events by relevance. The structure of the dataset is as follow: EBS: name of the event-based surveillance systems that detected the event (HealthMap or PADI-Web in this case) Country: Name of the country where the event happened Place_name: Name of the administration, city or district where the event happened Disease_name: Name of the disease that is reported in the article Species_name: Name of the affected host that is reported in the article Alert_id: Event identifier Source: Name of the news outlet reporting the article. href: URL information of the article reporting the considered event. Note that multiple article can report same event. Issue_date: Date of the article publication Manualclass: Manual classification (Relevant or Irrelevant) lon: Longitude for the spacial entity lat: Lattitude fot the spacial entity

  10. MERS Outbreaks data 2012-2019

    • kaggle.com
    Updated Jun 21, 2020
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    Devakumar K. P. (2020). MERS Outbreaks data 2012-2019 [Dataset]. https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019/tasks
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 21, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Devakumar K. P.
    Description

    forthebadge forthebadge

    Context

    • Middle East respiratory syndrome (MERS) is a viral respiratory illness
    • Caused by a coronavirus (Middle East respiratory syndrome coronavirus, or MERS‐CoV)
    • First identified in Saudi Arabia in 2012.
    • Coronaviruses are a large family of viruses that can cause diseases in humans,
    • Ranging from the common cold to Severe Acute Respiratory Syndrome (SARS).
    • Since 2012, MERS has been reported in 27 countries

    Content

    Each row contains a report from each region/location for each day Each column represents the number of cases reported from each country/region

    Inspiration

    To see how the epidemic spread worldwide in such a short time

    Acknowledgements / Data Source

    https://www.who.int/emergencies/mers-cov/en/ https://apps.who.int/iris/bitstream/handle/10665/326126/WHO-MERS-RA-19.1-eng.pdf?ua=1

    Collection methodology

    https://github.com/imdevskp/mers_dataset_collection_cleaning

    Cover Photo

    Photo from WHO website https://www.who.int/csr/disease/coronavirus_infections/maps-epicurves/en/

    Similar Datasets

  11. r

    Case, travel, socioeconomic and meteorological data for analysing...

    • researchdata.se
    • demo.researchdata.se
    Updated Jun 27, 2025
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    András Bota; Martin Holmberg; Lauren Gardner; Martin Rosvall (2025). Case, travel, socioeconomic and meteorological data for analysing socioeconomic and environmental patterns behind H1N1 spreading in Sweden [Dataset]. http://doi.org/10.5878/0hkf-tn97
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    (236422), (222425), (49996), (20328), (1891123), (6006), (43106)Available download formats
    Dataset updated
    Jun 27, 2025
    Dataset provided by
    Umeå University
    Authors
    András Bota; Martin Holmberg; Lauren Gardner; Martin Rosvall
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Time period covered
    2009 - 2015
    Area covered
    Sweden
    Description

    Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

    Publication available at: https://doi.org/10.1038/s41598-021-01857-4

    Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

    Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

    Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

    1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

    2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

    Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

    1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

    2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

  12. f

    Data Sheet 1_Spatial–temporal evolution patterns of influenza incidence in...

    • frontiersin.figshare.com
    • figshare.com
    zip
    Updated Apr 2, 2025
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    Sheng-lin Qin; Hai-jun Bai; Ping Deng; Yi-wen Wang; Song-ming Ma; Yang Zhang; Yu-qi Jiang; Jiang Long; Jin-hua Zhao (2025). Data Sheet 1_Spatial–temporal evolution patterns of influenza incidence in plateau regions from 2009 to 2023.zip [Dataset]. http://doi.org/10.3389/fpubh.2025.1553715.s001
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 2, 2025
    Dataset provided by
    Frontiers
    Authors
    Sheng-lin Qin; Hai-jun Bai; Ping Deng; Yi-wen Wang; Song-ming Ma; Yang Zhang; Yu-qi Jiang; Jiang Long; Jin-hua Zhao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ObjectivesThis study used (Geographic Information System) GIS technology to analyze the spatiotemporal distribution of influenza incidence in Qinghai from 2009 to 2023, based on influenza surveillance data.MethodsThis study first accessed the influenza data sets of Qinghai Province from 2009 to 2023 through the Chinese Infectious Disease Surveillance System. Subsequently, trend charts of influenza incidence in each city and prefecture were employed to illustrate the trends of influenza incidence during the period from 2009 to 2023. To explore the risks of influenza incidence in different counties and districts, methods including spatial autocorrelation, cluster analysis, hotspot analysis, Gravity center shift model, and standard deviation ellipse were utilized.ResultsThe study showed that the incidence of influenza showed significant fluctuations, with marked spikes in 2019 and 2023. Spatial autocorrelation analysis revealed significant positive autocorrelation in 2015, 2017–2019, and 2022–2023 (Moran’s I > 0 and p

  13. Preliminary 2024-2025 U.S. COVID-19 Burden Estimates

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Jul 11, 2025
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    Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD). (2025). Preliminary 2024-2025 U.S. COVID-19 Burden Estimates [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/Preliminary-2024-2025-U-S-COVID-19-Burden-Estimate/ahrf-yqdt
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    csv, application/rdfxml, json, application/rssxml, xml, tsvAvailable download formats
    Dataset updated
    Jul 11, 2025
    Dataset provided by
    National Center for Immunization and Respiratory Diseases
    Authors
    Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD).
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    United States
    Description

    This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

    Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

    References

    1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
    2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
    3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
    4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
    5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
    6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
    7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
    8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
    9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
    10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
    11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

  14. f

    Data set for the ILI and SARI patients enrolled in the NISSS in Tanzania,...

    • plos.figshare.com
    zip
    Updated Jun 21, 2023
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    Vulstan James Shedura; Ally Kassim Hussein; Salum Kassim Nyanga; Doreen Kamori; Geofrey Joseph Mchau (2023). Data set for the ILI and SARI patients enrolled in the NISSS in Tanzania, 2019. [Dataset]. http://doi.org/10.1371/journal.pone.0283043.s005
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Vulstan James Shedura; Ally Kassim Hussein; Salum Kassim Nyanga; Doreen Kamori; Geofrey Joseph Mchau
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Tanzania
    Description

    Data set for the ILI and SARI patients enrolled in the NISSS in Tanzania, 2019.

  15. Leading causes of death, total population, by age group

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
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    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

  16. nndss-table-1r-hepatitis-c-perinatal-infection-to

    • huggingface.co
    Updated May 30, 2019
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    Department of Health and Human Services (2019). nndss-table-1r-hepatitis-c-perinatal-infection-to [Dataset]. https://huggingface.co/datasets/HHS-Official/nndss-table-1r-hepatitis-c-perinatal-infection-to
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    Dataset updated
    May 30, 2019
    Dataset provided by
    United States Department of Health and Human Serviceshttp://www.hhs.gov/
    Authors
    Department of Health and Human Services
    License

    https://choosealicense.com/licenses/odbl/https://choosealicense.com/licenses/odbl/

    Description

    NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated pediatric mortality

      Description
    

    NNDSS - TABLE 1R. Hepatitis C, perinatal infection to Influenza-associated pediatric mortality - 2019. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents. Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/nndss-table-1r-hepatitis-c-perinatal-infection-to.

  17. Deaths by vaccination status, England

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 25, 2023
    + more versions
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    Office for National Statistics (2023). Deaths by vaccination status, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Aug 25, 2023
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

  18. Z

    SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019)

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jan 24, 2020
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    Samuel Soubeyrand (2020). SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_1410438
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    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Joseph Hughes
    Karine Berthier
    Samuel Soubeyrand
    Cécile Desbiez
    Gaël Thébaud
    Maryam Alamil
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links from deep sequencing data

    This archive contains R scripts for running analyses proposed by Alamil et al. (2019; Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases), namely - functions.R that contains R functions required for computations, - influenza.R, ebola.R and potyvirus.R where the analyses are implemented for each case study, and - influenza-format-genomic-data.R giving an example of how to format data to be used in the statistical learning approach.

    This archive also contains the reformatted data analyzed by Alamil et al. (2019). The datasets that are provided concern swine influenza virus (reformatted from Murcia et al., 2012), Ebola virus (reformatted from Gire et al., 2014) and a wild salsify potyvirus. Two rds files are provided for swine influenza, the first one for the naive chain, the second one for the vaccinated chain. Ebola rds files are compressed into the archive ebolaRDS.zip. rds files can be loaded in the R statistical software with the command "readRDS(filename)", which returns a list. The list contains a "readme" item describing the contents of the list, as well as a "host.table" item providing metadata about host units and a "set.of.sequences" item providing sequencing data formatted in numeric matrices.

    Murcia PR, Hughes J, Battista P, Lloyd L, Baillie GJ, Ramirez-Gonzalez RH, et al. Evolution of an Eurasian avian-like influenza virus in naive and vaccinated pigs. PLoS Pathogens. 2012;8(5):e1002730.

    Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369–1372

    Funded by the ANR - Project name: SMITID (2016-2020) - Grant number: ANR-16-CE35-0006

  19. Spearman correlation analysis of influenza incidence in Hubei from 2017 to...

    • plos.figshare.com
    xls
    Updated Nov 27, 2023
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    Mengmeng Yang; Shengsheng Gong; Shuqiong Huang; Xixiang Huo; Wuwei Wang (2023). Spearman correlation analysis of influenza incidence in Hubei from 2017 to 2019. [Dataset]. http://doi.org/10.1371/journal.pone.0280617.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Nov 27, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Mengmeng Yang; Shengsheng Gong; Shuqiong Huang; Xixiang Huo; Wuwei Wang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hubei
    Description

    Spearman correlation analysis of influenza incidence in Hubei from 2017 to 2019.

  20. f

    Spearman's correlation results between weekly meteorological variables and...

    • plos.figshare.com
    xls
    Updated Jun 11, 2023
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    Li Qi; Tian Liu; Yuan Gao; Dechao Tian; Wenge Tang; Qin Li; Luzhao Feng; Qiyong Liu (2023). Spearman's correlation results between weekly meteorological variables and confirmed influenza cases every ten thousand outpatient visits in Chongqing, China, 2012–2019. [Dataset]. http://doi.org/10.1371/journal.pone.0246023.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 11, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Li Qi; Tian Liu; Yuan Gao; Dechao Tian; Wenge Tang; Qin Li; Luzhao Feng; Qiyong Liu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Chongqing, China
    Description

    Spearman's correlation results between weekly meteorological variables and confirmed influenza cases every ten thousand outpatient visits in Chongqing, China, 2012–2019.

Share
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United States (2022). Provisional Death Counts for Influenza, Pneumonia, and COVID-19 [Dataset]. https://data.amerigeoss.org/es/dataset/provisional-death-counts-for-influenza-pneumonia-and-covid-19-d8b6f

Provisional Death Counts for Influenza, Pneumonia, and COVID-19

Explore at:
json, xml, csv, rdfAvailable download formats
Dataset updated
Aug 4, 2022
Dataset provided by
United States
License

https://www.usa.gov/government-workshttps://www.usa.gov/government-works

Description

Deaths counts for influenza, pneumonia, and coronavirus disease 2019 (COVID-19) reported to NCHS by week ending date, by state and HHS region, and age group.

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