Machine learning approaches to dissect hybrid and vaccine-induced immunity.

Journal: Communications medicine
Published Date: Jul 8, 2025

Abstract

BACKGROUND: The spread of SARS-CoV-2 Omicron variant and its subvariants, highly transmissible but responsible of milder disease, has increased unreported infection cases. Identifying unaware infected individuals is crucial for estimating the true prevalence of infection and evaluating the breadth of hybrid immunity. In this study, this challenge was addressed by applying several Machine Learning approaches.

Authors

  • Giorgio Montesi
    Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Simone Costagli
    Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.
  • Simone Lucchesi
    Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.
  • Jacopo Polvere
    Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Fabio Fiorino
    Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.
  • Gabiria Pastore
    Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.
  • Margherita Sambo
    Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Mario Tumbarello
    Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Massimiliano Fabbiani
    Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Francesca Montagnani
    Department of Medical Biotechnologies, University of Siena, Siena, Italy.
  • Donata Medaglini
    Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy. donata.medaglini@unisi.it.
  • Elena Pettini
    Department of Medical Biotechnologies, Laboratory of Molecular Microbiology and Biotechnology, University of Siena, Siena, Italy.
  • Annalisa Ciabattini
    Laboratory of Molecular Microbiology and Biotechnology, Department of Medical Biotechnologies, University of Siena, Siena, Italy. annalisa.ciabattini@unisi.it.

Keywords

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