Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk

  1. Sepehr Golriz Khatami
  2. Astghik Sargsyan
  3. Maria Francesca Russo
  4. Daniel Domingo-Fernández
  5. Andrea Zaliani
  6. Abish Kaladharan
  7. Priya Sethumadhavan
  8. Sarah Mubeen
  9. Yojana Gadiya
  10. Reagon Karki
  11. Stephan Gebel
  12. Ram Kumar Ruppa Surulinathan
  13. Vanessa Lage-Rupprecht
  14. Saulius Archipovas
  15. Geltrude Mingrone
  16. Marc Jacobs
  17. Carsten Claussen
  18. Martin Hofmann-Apitius
  19. Alpha Tom Kodamullil

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Abstract

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

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  1. Version published to 10.3390/data9020025
  2. ScreenIT

    SciScore for 10.1101/2021.11.14.21265797: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Using this clinical trial identifier tagger, we have searched in the CORD19 corpus and Medline 2020.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    In parallel to the collection of studies with any of the above-mentioned keywords, we also searched for publications in PubMed applying the search paradigms ‘serology’ and ‘serological profiling’ to specifically identify COVID-19 related articles with serological information.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    The web application was implemented in Python using Flask and several JavaScript libraries for visualization purposes including D3.js, DataTables, and DataMaps.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04299152Not yet recruitingStem Cell Educator Therapy Treat the Viral Inflammation in C…

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.11.14.21265797v1 on medRxiv