| @id | ./ |
|---|---|
| name [?] | BY-COVID WP5 T5.2 Baseline Use Case |
| @type | Dataset |
| description [?] | This publication corresponds to the Research Objects (RO) of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “COVID-19 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection”. |
| funder [?] | European Commission |
| datePublished [?] | 2023-04-19 |
| author [?] | |
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| codeRepository [?] | https://github.com/by-covid/BY-COVID_WP5_T5.2_baseline-use-case |
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| distribution [?] | https://github.com/by-covid/BY-COVID_WP5_T5.2_baseline-use-case/archive/refs/heads/main.zip |
| identifier [?] | https://doi.org/10.5281/zenodo.6913045 |
| cite-as [?] | https://w3id.org/ro/doi/10.5281/zenodo.6913045 |
| isBasedOn [?] | |
| keywords [?] | COVID-19, vaccines, comparative effectiveness, causal inference, international comparison, SARS-CoV-2, common data model, directed acyclic graph, synthetic data |
| license [?] | Creative Commons Attribution 4.0 International |
| dateModified [?] | 2023-10-05 |
| publisher [?] | BY-COVID |
| funding [?] | HORIZON-INFRA-2021-EMERGENCY-01 101046203 |
| mentions [?] |
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| url [?] | https://by-covid.github.io/BY-COVID_WP5_T5.2_baseline-use-case/ |
| version [?] | 1.2.0 |
| assesses [?] | Research Question: How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections? |
| material [?] | Cohort definition: All individuals (from 5 to 115 years old, included) vaccinated with at least one dose of the SARS-CoV-2 vaccine (any of the available brands) and all individuals eligible to be vaccinated with a documented positive diagnosis (irrespective of the type of test) for a SARS-CoV-2 infection during the data extraction period. |
| materialExtent [?] | Inclusion criteria: All people vaccinated with at least one dose of the COVID-19 vaccine (any of the available brands) in an area of residence. Any person eligible to be vaccinated (from 5 to 115 years old, included) with a positive diagnosis (irrespective of the type of test) for SARS-CoV-2 infection (COVID-19) during the period of data extraction. Exclusion criteria: People not eligible for the vaccine (from 0 to 4 years old, included) |
| publishingPrinciples [?] | Study Design: An observational retrospective longitudinal study to assess the effectiveness of the SARS-CoV-2 vaccines in preventing SARS-CoV-2 infections using routinely collected social, health and care data from several countries. A causal model was established using Directed Acyclic Graphs (DAGs) to map domain knowledge, theories and assumptions about the causal relationship between exposure and outcome. |
| temporalCoverage [?] | Study Period: From the date of the first documented SARS-CoV-2 infection in each country to the most recent date in which data is available at the time of analysis. Roughly from 01-03-2020 to 30-06-2022, depending on the country. |
| usageInfo [?] | The scripts (software) included in the publication are offered "as-is", without warranty, and disclaiming liability for damages resulting from using it. The software is released under the CC-BY-4.0 licence, which gives you permission to use the content for almost any purpose (but does not grant you any trademark permissions), so long as you note the license and give credit. |
| releaseNotes [?] | - Updated Causal model to eliminate the consideration of 'vaccination_schedule_cd' as a mediator - Adjusted the study period to be consistent with the Study Protocol - Updated 'sex_cd' as a required variable - Added 'chronic_liver_disease_bl' as a comorbidity at the individual level - Updated 'socecon_lvl_cd' at the area level as a recommended variable -Added crosswalks for the definition of 'chronic_liver_disease_bl' in a separate sheet -Updated the 'vaccination_schedule_cd' reference to the 'Vaccine' node in the updated DAG -Updated the description of the 'confirmed_case_dt' and 'previous_infection_dt' variables to clarify the definition and the need for a single registry per person |
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| @id | https://ror.org/00k4n6c32 |
|---|---|
| name [?] | European Commission |
| @type | Organization |
| Items that reference this one | |
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| @id | https://orcid.org/0000-0002-6285-8120 |
|---|---|
| name [?] | Francisco Estupiñán-Romero |
| @type | Person |
| affiliation [?] | Instute for Health Science in Aragon (IACS) |
| email [?] | festupinnan.iacs@aragon.es |
| Items that reference this one | |
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|---|---|
| name [?] | Nina Van Goethem |
| @type | Person |
| affiliation [?] | Sciensano |
| email [?] | Nina.VanGoethem@sciensano.be |
| Items that reference this one | |
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| name [?] | Enrique Bernal-Delgado |
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| affiliation [?] | Instute for Health Science in Aragon (IACS) |
| email [?] | ebernal.iacs@aragon.es |
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| @id | vaccine_effectiveness_analytical_pipeline/ |
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| name [?] | BY-COVID - WP5 - Baseline Use Case: SARS-CoV-2 vaccine effectiveness assessment - Analytical pipeline |
| @type | Dataset |
| description [?] | Reproducable analytical pipeline to apply causal inference techniques using distributed observational data |
| datePublished [?] | 2023-04-26 |
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| version [?] | 1.0.0 |
| url [?] | https://github.com/by-covid/BY-COVID_WP5_T5.2_baseline-use-case/tree/main/vaccine_effectiveness_analytical_pipeline |
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| description [?] | Causal model responding to the research question, using a Directed Acyclic Graph |
| datePublished [?] | 2023-01-26 |
| version [?] | 1.1.0 |
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| description [?] | Synthetic data set to test the causal model proposed by the baseline use case in BY-COVID WP5 assessing the effectiveness of the COVID-19 vaccine(s) |
| datePublished [?] | 2023-01-26 |
| version [?] | 1.1.0 |
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| mainEntity [?] | Quarto script sourcing consecutive scripts of the analytical pipeline (QMD) |
| spatialCoverage [?] | Europe |
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| datePublished [?] | 2023-02-09 |
| version [?] | 0.0.1 |
| identifier [?] | https://doi.org/10.5281/zenodo.7625783 |
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| datePublished [?] | 2023-04-13 |
| version [?] | 1.0.3 |
| identifier [?] | https://doi.org/10.5281/zenodo.7551181 |
| url [?] | https://zenodo.org/record/7825979 |
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| image [?] | Quarto script sourcing consecutive scripts of the analytical pipeline (QMD) |
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| description [?] | From Causal model, via a data model spec to generation of synthetic data |
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| description [?] | BeYond-COVID, make data from COVID-19 and other infectious diseases open and accessible to everyone |
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