Open Epidemiology for pandemic modelling: a transparent, traceable, reusable, open source pipeline for reproducible science

  • Funded by UK Research and Innovation (UKRI)
  • Total publications:5 publications

Grant number: ST/V006126/1

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2021
    2022
  • Known Financial Commitments (USD)

    $679,847.85
  • Funder

    UK Research and Innovation (UKRI)
  • Principal Investigator

    Richard Reeve
  • Research Location

    United Kingdom
  • Lead Research Institution

    University of Glasgow
  • Research Priority Alignment

    N/A
  • Research Category

    Epidemiological studies

  • Research Subcategory

    N/A

  • Special Interest Tags

    Data Management and Data Sharing

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Historically, models used to support advice to government have not been publicly available, at least not readily, prior to publication. Technological advances and the growth of open source and reproducible science mean this is no longer tenable. Although current models feeding into UK policy are publicly available, they still lack the transparent and readily traceable chain of evidence connecting data and assumptions with model outputs that allows them to be readily independently assessed. Our Data Pipeline supports the implementation of COVID-19 epidemiological models that we, the Scottish COVID-19 Response Consortium (SCRC), have developed using volunteer resources within the RAMP initiative, to create new, complementary models. The Data Pipeline fulfils a critical role in our assessment of fitness for purpose for the models in providing policy advice, by managing and documenting a chain of trust that connects the primary data, analyses, and published and unpublished literature on COVID-19 to model outputs, documenting provenance of the conclusions being reached. The software interfaces we develop will be powerful, generic tools that will be useful to any policy-oriented modelling community.

Publicationslinked via Europe PMC

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View all publications at Europe PMC

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

Trends in the diversity of mortality causes and age-standardised mortality rates among subpopulations within Scotland, 2001-2019.

The challenges of data in future pandemics.

Complex model calibration through emulation, a worked example for a stochastic epidemic model.