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

Sepehr Golriz Khatami, Astghik Sargsyan, Maria Francesca Russo, Daniel Domingo-Fernández, Andrea Zaliani, Abish Kaladharan, Priya Sethumadhavan, Sarah Mubeen, Yojana Gadiya, Reagon Karki, Stephan Gebel, Ram Kumar Ruppa Surulinathan, Vanessa Lage-Rupprecht, Saulius Archipovas, Geltrude Mingrone, Marc Jacobs, Carsten Claussen, Martin Hofmann-Apitius and Alpha Tom Kodamullil ()
Additional contact information
Sepehr Golriz Khatami: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Astghik Sargsyan: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Maria Francesca Russo: Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Daniel Domingo-Fernández: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Andrea Zaliani: Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany
Abish Kaladharan: Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin 683503, India
Priya Sethumadhavan: Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin 683503, India
Sarah Mubeen: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Yojana Gadiya: Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany
Reagon Karki: Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany
Stephan Gebel: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Ram Kumar Ruppa Surulinathan: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Vanessa Lage-Rupprecht: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Saulius Archipovas: Fraunhofer Institute for Digital Medicine (MEVIS), 28359 Bremen, Germany
Geltrude Mingrone: Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Marc Jacobs: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Carsten Claussen: Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 22525 Hamburg, Germany
Martin Hofmann-Apitius: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany
Alpha Tom Kodamullil: Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757 Sankt Augustin, Germany

Data, 2024, vol. 9, issue 2, 1-19

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.

Keywords: COVID-19; data catalogue; personalized risk model; predictive models (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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