Modelling the long-term health impact of COVID-19 using Graphical Chain Models
The biological plausibility of the relationships between variables demonstrated by the GCM validates the efficacy of this approach as a valuable statistical tool for elucidating structural features, such as conditional dependencies and associations. This promising method holds potential for exploring the long-term health repercussions of COVID-19 by identifying predictive factors and establishing suitable therapeutic strategies.
Authors
- C. Caroni
- Irene Dominioni
- Maria Bruna Pasticci
- Roberto Ferranti
- Elena Stanghellini
- Ilenia Folletti
- Gaetano Vaudo (ORCID: https://orcid.org/0000-0002-3176-2038)
- Leandro Sanesi
- Francesco Bartolucci (ORCID: https://orcid.org/0000-0001-7057-1421)
- Stefano Bartoli (ORCID: https://orcid.org/0000-0001-8133-1802)
- E. Santoni
- Maria Cristina Cavallo
- Giacomo Pucci (ORCID: https://orcid.org/0000-0003-0180-859X)
- Giovanni Morgana
- Konstantina Gourgoura
- P. Rivadeneyra
- Raquel Curcio
Institutions
- Università di Camerino (IT)
- University of Padua (IT)
- National Technical University of Athens (GR)
- University of Perugia (IT)
- Santa Maria Nuova Hospital (IT)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1186/s12879-024-09777-0
- Citations
- 2
- Primary Topic
- Long-Term Effects of COVID-19
- Type
- article
- Field-Weighted Citation Impact
- 0.74
Funders
- Fondazione Cassa di Risparmio di Perugia