100+ datasets found
  1. Data from: Economic modelling of forced saving during the coronavirus...

    • s3.amazonaws.com
    • gov.uk
    Updated Jun 6, 2022
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    Office for National Statistics (2022). Economic modelling of forced saving during the coronavirus (COVID-19) pandemic [Dataset]. https://s3.amazonaws.com/thegovernmentsays-files/content/181/1814103.html
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    Dataset updated
    Jun 6, 2022
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Office for National Statistics
    Description

    Official statistics are produced impartially and free from political influence.

  2. N

    MIDAS Online Portal for COVID-19 Modeling Research

    • datacatalog.med.nyu.edu
    Updated Sep 26, 2022
    + more versions
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    MIDAS Coordination Center (2022). MIDAS Online Portal for COVID-19 Modeling Research [Dataset]. https://datacatalog.med.nyu.edu/dataset/10402
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    Dataset updated
    Sep 26, 2022
    Dataset authored and provided by
    MIDAS Coordination Center
    Time period covered
    Jan 1, 2020 - Present
    Area covered
    International
    Description

    The MIDAS (Models of Infectious Disease Agent Study) Online Portal for COVID-19 Modeling Research is a collection of publicly-available COVID-19 resources to support dashboard monitoring, data processing, modeling, and visualization efforts. Collections listed in the portal include case counts and case line lists with documented metadata, peer-reviewed and non-peer-reviewed parameter estimates, and software created by MIDAS community members. Datasets and parameter estimates are maintained and stored in the MIDAS Github repository; software is hosted by their respective creators on Github or a personal webpage.

  3. CDPH-CalCAT Modeling Nowcasts and Forecasts for COVID-19 and Influenza

    • data.ca.gov
    • data.chhs.ca.gov
    • +3more
    csv, parquet, zip
    Updated Jul 4, 2025
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    California Department of Public Health (2025). CDPH-CalCAT Modeling Nowcasts and Forecasts for COVID-19 and Influenza [Dataset]. https://data.ca.gov/dataset/cdph-calcat-modeling-nowcasts-and-forecasts-for-covid-19-and-influenza
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    csv, zip, parquetAvailable download formats
    Dataset updated
    Jul 4, 2025
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    License

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

    Description

    This dataset includes three tables with the model-based projections and estimates as shown on CalCAT in 2025 (http://calcat.cdph.ca.gov) for California state, regions, and counties.

    (1) COVID-19 Nowcasts includes the R-effective estimates for COVID-19 from the different models available for the past 80 days from the archive date and the median ensemble thereof.

    (2) CalCAT Forecasts includes hospital census and admissions forecasts for COVID-19 and Influenza, and the corresponding ensemble metrics for a 4 week horizon from the archive date.

    (3) Variant Proportion Nowcasts contains the Integrated Genomic Epidemiology Dataset (IGED)-based and Terra-based estimates of COVID-19 variants circulating over the past 3 months as well as model-based predictions for the proportions of the variants of concern for dates leading up to the archive date. Prediction intervals are included when available.

    This dataset provides CalCAT users with programmatic access to the downloadable datasets on CalCAT.

    This dataset also includes a zipped file with the historical archives of the COVID-19 Nowcasts, CalCAT Forecasts and Variant Proportion Nowcasts through 2023.

  4. h

    Supporting data for "Investigating Changes of COVID-19 Epidemiological...

    • datahub.hku.hk
    zip
    Updated Mar 29, 2025
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    Dongxuan Chen (2025). Supporting data for "Investigating Changes of COVID-19 Epidemiological Parameters from Different Perspectives" [Dataset]. http://doi.org/10.25442/hku.27929508.v1
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    zipAvailable download formats
    Dataset updated
    Mar 29, 2025
    Dataset provided by
    HKU Data Repository
    Authors
    Dongxuan Chen
    License

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

    Description

    My PhD thesis with title "Investigating Changes in COVID-19 Epidemiological Parameters from Different Perspectives" focus on using line list data (anonymized), patient hospitalization data (anonymized) and viral load data (anonymized) to improve the estimatin of different key epidemiological parameters during the COVID-19 pandemic in Hong Kong.This dataset contains supporting data for reproducibility, it has 6 subfolders correspond to 6 chapters of the thesis (chapters 2, 4, 5, 6, 7, 8) where contain figures and data analyses, each sub folder contains data and R code for reproducing the figures and other analytical results, with README file accompanied with each sub folder.In chapter 2, I provided an overview of the COVID-19 pandemic in Hong Kong and worldwide, and thus used datasets contain case incidence data and a R code to generate incidence figure. I also conducted a systematic review of the latent period estimation, and I provided the endnote library with spreadsheet of the endnote output that contain my paper screening process, which are included in subfolder dataset chapter 2.In chapter 4, I did a detailed statistical analyses of the changing serial interval of COVID-19 in Hong Kong, and thus sub folder dataset chapter 4 contained anonymized transmission pair line list data for estimating the serial interval, I provided R codes and essential subset of the data output for reproducibility of my results. The related published work is on American Journal of Epidemiology, in README chapter4.txt I have put the DOI of this paper.In chapter 5, I developed an inferential framework to infer the generation interval on temporal time scale, sub folder dataset chapter 5 contained public available line list data from mainland China, and R codes and essential subset of the data output for reproducibility of my results. The related published work is on Nature Communications, and the data and code are also available on github, I have out the DOI and github link in README chapter5.txt.In chapter 6, I investigated the superspreading potential and setting-specific generation interval in Hong Kong, subfolder dataset chapter 6 contained simplified and anonymized transmission cluster size information, and related R code to reproduce the result, and also the R code for modelling buildig and estimation summary of the generation interval estimates.In chapter 7, I estimated the latent period of COVID-19 based on different settings in Hong Kong, sub folder dataset chapter 7 contained processed and anonymized viral load record and transmission pair information of COVID-19 cases in Hong Kong, and related R code to reproduce the result, together with two spreadsheets for estimation summary. The entire R programming process contain a lot of R scripts, which I put two sub folders (R and Stan) under sub folder dataset chapter 7, and also put the original Github link for R programming of the method in README chapter 7.txtIn chapter 8, I analyzed the length of stay in hospital of COVID-19 patients in Hong Kong and the potential association with vaccination status. In sub folder dataset chapter 8 I put a simplified and anonymized dataset of patient's hospitalization record regarding their vaccination status and length of stay in hospital for the analysis. I also put R code and essential subset of the data output to reproduce the result.

  5. Global model data generated for COVID-19 simulations 2012-2014

    • catalogue.ceda.ac.uk
    • data-search.nerc.ac.uk
    Updated Aug 28, 2020
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    John Staunton Sykes; Youngsub Shin; James Weber; Alex Archibald; Luke Abraham; Scott Archer-Nicholls (2020). Global model data generated for COVID-19 simulations 2012-2014 [Dataset]. https://catalogue.ceda.ac.uk/uuid/b5ea7341a7164525b74143d8afe77223
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    Dataset updated
    Aug 28, 2020
    Dataset provided by
    Centre for Environmental Data Analysishttp://www.ceda.ac.uk/
    Authors
    John Staunton Sykes; Youngsub Shin; James Weber; Alex Archibald; Luke Abraham; Scott Archer-Nicholls
    License

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

    Time period covered
    Jan 1, 2012 - Dec 31, 2014
    Area covered
    Earth
    Variables measured
    time, sigma, latitude, longitude, level_height, pseudo_level, forecast_period, model_level_number, forecast_reference_time, toa_incoming_shortwave_flux, and 17 more
    Description

    Global model data has been generated for COVID-19 (Coronavirus Disease 2019) simulations. The model used was the United Kingdom Earth System Model 1.0 (UKESM1.0), in an atmosphere-only nudged configuration, with Met Office Unified Model version 11.5. The data is on a global N96 grid (192 x 144 points), and covers the years 2012, 2013, and 2014. These data were used to study the effect of COVID-19 lockdowns (simulated scenarios) on atmospheric composition and radiative forcing.

    The dataset includes data used in the paper submitted to Geophysical Research Letters (GRL) August 2020 with title 'Minimal climate impacts from short-lived climate forcers following emission reductions related to the COVID-19 pandemic'. See Details/Docs tab for a link to this. For this purpose, there are four experimental integrations (a1, a2, a3, a4), and a control (con) for each year. The files are labelled using variable codes such as m01s34i001 to determine the model variable field contained. A full description of what these are can be found in the included docs/file variable_codes.txt.

    The data are in NetCDF format, and were generated from the following suites: u-bt034, u-bt090, u-bt091, u-bt092, u-bt637, u-bt341, u-bt342, u-bt343, u-bt344, u-bt926, u-bt375, u-bt376, u-bt377, u-bt378, u-bt927.

    This is a NERC funded project.

  6. f

    Estimate, standard error (SE) and 95% confidence interval of peak statistics...

    • figshare.com
    xls
    Updated May 30, 2023
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    Chénangnon Frédéric Tovissodé; Bruno Enagnon Lokonon; Romain Glèlè Kakaï (2023). Estimate, standard error (SE) and 95% confidence interval of peak statistics using the COVID-19 daily case reporting data from Italy (2020-02-20 to 2020-07-11). [Dataset]. http://doi.org/10.1371/journal.pone.0240578.t002
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Chénangnon Frédéric Tovissodé; Bruno Enagnon Lokonon; Romain Glèlè Kakaï
    License

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

    Description

    Estimate, standard error (SE) and 95% confidence interval of peak statistics using the COVID-19 daily case reporting data from Italy (2020-02-20 to 2020-07-11).

  7. m

    Generated Prediction Data of COVID-19's Daily Infections in Brazil

    • data.mendeley.com
    Updated Jul 12, 2020
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    Mohamed Hawas (2020). Generated Prediction Data of COVID-19's Daily Infections in Brazil [Dataset]. http://doi.org/10.17632/t2zk3xnt8y.1
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    Dataset updated
    Jul 12, 2020
    Authors
    Mohamed Hawas
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    Dataset general description:

    • This dataset reports 4195 recurrent neural network models, their settings, and their generated prediction csv files, graphs, and metadata files, for predicting COVID-19's daily infections in Brazil by training on limited raw data (30 time-steps and 40 time-steps alternatives). The used code is developed by the author and located in the following online data repository link: http://dx.doi.org/10.17632/yp4d95pk7n.1

    Dataset content:

    • Models, Graphs, and csv predictions files: 1. Deterministic mode (DM): includes 1194 generated models files (30 time-steps), and their generated 2835 graphs and 2835 predictions files. Similarly, this mode includes 1976 generated model files (40 time-steps), and their generated 7301 graphs and 7301 predictions files. 2. Non-deterministic mode (NDM): includes 20 generated model files (30 time-steps), and their generated 53 graphs and 53 predictions files. 3. Technical validation mode (TVM): includes 1001 generated model files (30 time-steps), and their generated 3619 graphs and 3619 predictions files for 358 models, which are a sample of 1001 models. Also, 1 model in control group for India. 4. 1 graph and 1 prediction files for each of DM and NDM, reporting evaluation till 2020-07-11.

    • Settings and metadata for the above 3 categories: 1. Used settings in json files for reproducibility. 2. Metadata about training and prediction setup and accuracy in csv files.

    Raw data source that was used to train the models:

    • The used raw data for training the models is from: COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University): https://github.com/CSSEGISandData/COVID-19

    • The models were trained on these versions of the raw data: 1. Link till 2020-06-29 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/78d91b2dbc2a26eb2b2101fa499c6798aa22fca8/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv 2. Link till 2020-06-13 (accessed 2020-07-08): https://github.com/CSSEGISandData/COVID-19/raw/02ea750a263f6d8b8945fdd3253b35d3fd9b1bee/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

    License: This prediction Dataset is licensed under CC BY NC 3.0.

    Notice and disclaimer: 1- This prediction Dataset is for scientific and research purposes only. 2- The generation of this Dataset complies with the terms of use of the publicly available raw data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University: https://github.com/CSSEGISandData/COVID-19 and therefore, the author of the prediction Dataset disclaims any and all responsibility and warranties regarding the contents of used raw data, including but not limited to: the correctness, completeness, and any issues linked to third-party rights.

  8. "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level"...

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated Jan 21, 2021
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    Matthew Merski; Matthew Merski (2021). "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. dataset [Dataset]. http://doi.org/10.5281/zenodo.4078975
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    binAvailable download formats
    Dataset updated
    Jan 21, 2021
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Matthew Merski; Matthew Merski
    License

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

    Description

    Raw, original data and fits data set for "A Simple Model to Predict Future SARS-CoV-2 Infections on a National Level" by Blanco et al. in EXCEL and GraphPad Prism file formats.

  9. CDC COVID-19 Cases and Deaths Ensemble Forecast Archive

    • healthdata.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Apr 27, 2023
    + more versions
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    data.cdc.gov (2023). CDC COVID-19 Cases and Deaths Ensemble Forecast Archive [Dataset]. https://healthdata.gov/dataset/CDC-COVID-19-Cases-and-Deaths-Ensemble-Forecast-Ar/hjhg-fag8
    Explore at:
    application/rdfxml, json, csv, tsv, application/rssxml, xmlAvailable download formats
    Dataset updated
    Apr 27, 2023
    Dataset provided by
    data.cdc.gov
    Description

    This dataset contains forecasted weekly numbers of reported COVID-19 incident cases, incident deaths, and cumulative deaths in the United States, previously reported on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#datatracker-home). These forecasts were generated using mathematical models by CDC partners in the COVID-19 Forecast Hub (https://covid19forecasthub.org/doc/ensemble/). A CDC ensemble model was produced every week using the submitted models from that week at the national, and state/territory level.

    This dataset is intended to mirror the observed and forecasted data, previously available for download on the CDC’s COVID Data Tracker. Mortality forecasts for both new and cumulative reported COVID-19 deaths were produced at the state and territory level and national level. Forecasts of new reported COVID-19 cases were produced at the county, state/territory, and national level. Please note that this dataset is not complete for every model, date, location or combination thereof. Specifically, county level submissions for COVID-19 incident cases were accepted, but not required, and are missing or incomplete for many models and dates. State and territory-level forecasts are more complete, but not all models submitted forecasts for all locations, dates, and targets (new reported deaths, new reported cases, and cumulative reported deaths). Forecasts for COVID-19 incident cases were discontinued in February 2022. Forecasts for COVID-19 cumulative and incident deaths were discontinued in March 2023.

  10. m

    Example 4: l-i SEIR-Vaccination model - Effect of Vaccination on COVID-19...

    • data.mendeley.com
    Updated Jul 20, 2022
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    Xiaoping Liu (2022). Example 4: l-i SEIR-Vaccination model - Effect of Vaccination on COVID-19 Spread in the United States [Dataset]. http://doi.org/10.17632/f6s2dw9mrn.1
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    Dataset updated
    Jul 20, 2022
    Authors
    Xiaoping Liu
    License

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

    Description

    In examples 1 to 3, we have demonstrated how to use Excel to calculate variables Sn, En, In, Rn, yn in l-i SEIR (Susceptible-Exposed-Infectious-Recovered) model, to determine the time-dependent kn, and to find the number of actual total infections in the absence of vaccination and breakthrough infections. In the l-i SEIR model, l is the time length of latent period, i is the time length of infectious period, and yn is the number of daily-confirmed cases of infections. In this section (Example 4), we will extend l-i SEIR model to l-i SEIR-vaccination model for examining the effect of vaccination on COVID-19 transmission. Two files (one Word file and one Excel files) are attached. In the Word file, the author described how to build the l-i SEIR-vaccination model and how to calculate the number of daily confirmed cases of COVID-19 infections, yn, in Excel. The calculated yn and the reported yn have been compared to each other and displayed graphically in the Excel file

  11. COVID-19 Visualisation and Epidemic Analysis Data

    • kaggle.com
    Updated Jan 24, 2021
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    Dylan Shen (2021). COVID-19 Visualisation and Epidemic Analysis Data [Dataset]. https://www.kaggle.com/dylansp/covid19-country-level-data-for-epidemic-model/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 24, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Dylan Shen
    License

    http://opendatacommons.org/licenses/dbcl/1.0/http://opendatacommons.org/licenses/dbcl/1.0/

    Description

    COVID-19 Dataset for Epidemic Model Development

    I combined several data sources to gain an integrated dataset involving country-level COVID-19 confirmed, recovered and fatalities cases which can be used to build some epidemic models such as SIR, SIR with mortality. Adding information regarding population which can be used for calculating incidence rate and prevalence rate. One of my applications based on this dataset is published at https://dylansp.shinyapps.io/COVID19_Visualization_Analysis_Tool/.

    Content

    My approach is to retrieve cumulative confirmed cases, fatalities and recovered cases since 2020-01-22 onwards from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) COVID-19 dataset, merged with country code as well as population of each country. For the purpose of building epidemic models, I calculated information regarding daily new confirmed cases, recovered cases, and fatalities, together with remaining confirmed cases which equal to cumulative confirmed cases - cumulative recovered cases - cumulative fatalities. I haven't yet to find creditable data sources regarding probable cases of various countries yet. I'll add them once I found them.

    • Date: The date of the record.
    • Country_Region: The name of the country/region. -alpha-3_code: country code for that can be used for map visualization.
    • Population: The population of the given country/region.
    • Total_Confirmed_Cases: Cumulative confirmed cases.
    • Total_Fatalities: Cumulative fatalities.
    • Total_Recovered_Cases: Cumulative recovered cases.
    • New_Confirmed_Cases: Daily new confirmed cases.
    • New_Fatalities: Daily new fatalities.
    • New_Recovered_Cases: Daily new recovered cases.
    • Remaining_Confirmed_Cases: Remaining infected cases which equal to (cumulative confirmed cases - cumulative recovered cases - cumulative fatalities).

    Acknowledgements

    1. The data source of confirmed cases, recovered cases and deaths is JHU CSSE https://github.com/CSSEGISandData/COVID-19;
    2. The data source of the country-level population mainly comes from https://storage.guidotti.dev/covid19/data/ and Worldometer (https://www.worldometers.info/population/).

    Inspiration

    1. Building up the country-level COVID-19 case track dashboard.
    2. Insights regarding the incidence rate, prevalence rate, mortality and recovery rate of various countries.
    3. Building up epidemic models for forecasting.
  12. n

    Data from: Global-scale modeling of early factors and country-specific...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Nov 29, 2022
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    Sujoy Ghosh (2022). Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic [Dataset]. http://doi.org/10.5061/dryad.612jm6465
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    zipAvailable download formats
    Dataset updated
    Nov 29, 2022
    Dataset provided by
    Duke-NUS Medical School
    Authors
    Sujoy Ghosh
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    Studies examining factors responsible for COVID-19 incidence are mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. Univariate analysis identified eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country. Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks. Methods Data for COVID-19 confirmed cases was obtained from https://ourworldindata.org/coronavirus-source-data, which is updated daily and based on data on confirmed cases and deaths from Johns Hopkins University. Data on additional demographic, meteorological, health or economic variables were downloaded from a variety of sources online. For each variable, values from the most recent year for which data on the greatest number of countries were available were utilized (varied between 2016-2019). Variables were categorized as Demographic, Meterological, Health or Economic domains. Please see the README document ("README_data_COVID19_112322.txt") and the accompanying published article: Ghosh, S., Roy, S.S. Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic. BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12889-022-14336-w

  13. m

    COVID-19 data by States in Nigeria

    • data.mendeley.com
    Updated May 25, 2021
    + more versions
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    Ezekiel Ogundepo (2021). COVID-19 data by States in Nigeria [Dataset]. http://doi.org/10.17632/pvtwdz8npt.1
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    Dataset updated
    May 25, 2021
    Authors
    Ezekiel Ogundepo
    License

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

    Area covered
    Nigeria
    Description

    This dataset was collected from the official website of the Nigeria Centre for Disease Control (NCDC) provides the daily incidence of COVID-19 from February 23, 2020, to April 10, 2021, were organised in a spreadsheet to build a daily time-series database. The dataset also contains population per state in Nigeria, COVID-19 testing laboratories, etc.

  14. I

    Data from: Learning and Predicting from Dynamic Models for COVID-19 Patient...

    • data.niaid.nih.gov
    • immport.org
    url
    Updated Dec 12, 2024
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    (2024). Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring [Dataset]. http://doi.org/10.21430/M3U1359IJE
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    urlAvailable download formats
    Dataset updated
    Dec 12, 2024
    License

    https://www.immport.org/agreementhttps://www.immport.org/agreement

    Description

    COVID-19 has challenged health systems to learn how to learn. This paper describes the context, methods and challenges for learning to improve COVID-19 care at one academic health center. Challenges to learning include: (1) choosing a right clinical target; (2) designing methods for accurate predictions by borrowing strength from prior patients' experiences; (3) communicating the methodology to clinicians so they understand and trust it; (4) communicating the predictions to the patient at the moment of clinical decision; and (5) continuously evaluating and revising the methods so they adapt to changing patients and clinical demands. To illustrate these challenges, this paper contrasts two statistical modeling approaches - prospective longitudinal models in common use and retrospective analogues complementary in the COVID-19 context - for predicting future biomarker trajectories and major clinical events. The methods are applied to and validated on a cohort of 1,678 patients who were hospitalized with COVID-19 during the early months of the pandemic. We emphasize graphical tools to promote physician learning and inform clinical decision making.

  15. o

    Data from: Modeling the early transmission of COVID-19 in New York and San...

    • explore.openaire.eu
    Updated Jan 1, 2022
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    Shanshan Feng; Xiao-Feng Luo; Xin Pei; Zhen Jin; Mark Lewis; Hao Wang (2022). Modeling the early transmission of COVID-19 in New York and San Francisco using a pairwise network model [Dataset]. https://explore.openaire.eu/search/other?orpId=od_1875::df49353b5ca61ec52a5606da9e89c4d9
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    Dataset updated
    Jan 1, 2022
    Authors
    Shanshan Feng; Xiao-Feng Luo; Xin Pei; Zhen Jin; Mark Lewis; Hao Wang
    Area covered
    San Francisco, New York
    Description

    Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.

  16. COVID-19 Models Raw Data

    • kaggle.com
    zip
    Updated May 7, 2020
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    inversion (2020). COVID-19 Models Raw Data [Dataset]. https://www.kaggle.com/inversion/covid19-models-raw-data
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    zip(16137470 bytes)Available download formats
    Dataset updated
    May 7, 2020
    Authors
    inversion
    Description

    Dataset

    This dataset was created by inversion

    Contents

    It contains the following files:

  17. Corresponding to the second COVID-19 data, the goodness of fit measures of...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Xiaofeng Liu; Zubair Ahmad; Ahmed M. Gemeay; Alanazi Talal Abdulrahman; E. H. Hafez; N. Khalil (2023). Corresponding to the second COVID-19 data, the goodness of fit measures of the fitted models. [Dataset]. http://doi.org/10.1371/journal.pone.0254999.t011
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Xiaofeng Liu; Zubair Ahmad; Ahmed M. Gemeay; Alanazi Talal Abdulrahman; E. H. Hafez; N. Khalil
    License

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

    Description

    Corresponding to the second COVID-19 data, the goodness of fit measures of the fitted models.

  18. I

    Data from: Modeling the impact of racial and ethnic disparities on COVID-19...

    • data.niaid.nih.gov
    url
    Updated Oct 26, 2023
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    Marc Lipsitch (2023). Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics [Dataset]. http://doi.org/10.21430/M33DAWUEID
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    urlAvailable download formats
    Dataset updated
    Oct 26, 2023
    Dataset provided by
    Harvard University
    Authors
    Marc Lipsitch
    License

    https://www.immport.org/agreementhttps://www.immport.org/agreement

    Description

    Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown. Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk change across groups. Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites. Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection. Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation. M.L. was supported by SeroNet cooperative agreement U01 CA261277

  19. M

    COVID-19 Scenario Modeling Hub

    • catalog.midasnetwork.us
    csv
    Updated Jul 12, 2023
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    MIDAS Coordination Center (2023). COVID-19 Scenario Modeling Hub [Dataset]. https://catalog.midasnetwork.us/?object_id=318
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jul 12, 2023
    Dataset authored and provided by
    MIDAS Coordination Center
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Variables measured
    disease, COVID-19, modeling, pathogen, case counts, Homo sapiens, host organism, mortality data, infectious disease, hospital stay dataset, and 1 more
    Dataset funded by
    National Institute of General Medical Sciences
    Description

    The COVID-19 Scenario Hub contains a standardized set of data on scenario projections from teams making projections of cumulative and incident deaths and incident hospitalizations due to COVID-19 in the United States. The Scenario Hub harmonizes scenario projections in the United States to generate long-term COVID-19 projections combining insights from different models and in order to make them available to decision-makers, public health experts, and the general public.

  20. G

    Mathematical modelling and COVID-19

    • ouvert.canada.ca
    • open.canada.ca
    html
    Updated Sep 24, 2021
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    Public Health Agency of Canada (2021). Mathematical modelling and COVID-19 [Dataset]. https://ouvert.canada.ca/data/dataset/f659d98a-4945-4ace-9c3f-35226d4a8037
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    htmlAvailable download formats
    Dataset updated
    Sep 24, 2021
    Dataset provided by
    Public Health Agency of Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Mathematical modelling of COVID-19 pandemic which uses mathematical equations to estimate how many cases of a disease may occur in the coming weeks or months.

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Office for National Statistics (2022). Economic modelling of forced saving during the coronavirus (COVID-19) pandemic [Dataset]. https://s3.amazonaws.com/thegovernmentsays-files/content/181/1814103.html
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Data from: Economic modelling of forced saving during the coronavirus (COVID-19) pandemic

Related Article
Explore at:
Dataset updated
Jun 6, 2022
Dataset provided by
GOV.UKhttp://gov.uk/
Authors
Office for National Statistics
Description

Official statistics are produced impartially and free from political influence.

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