An informed deep learning model of the Omicron wave and the impact of vaccination.

Journal: Computers in biology and medicine
PMID: 40209576

Abstract

The Omicron (B.1.1.529) variant of SARS-CoV-2 emerged in November 2021 and has since evolved into multiple lineages. Understanding its transmission, vaccine efficacy, and potential for reinfection is crucial. This study examines the dynamics of Omicron in Germany, France, and Italy by employing Physics-Informed Neural Networks to estimate the temporal parameters influencing its spread. We validated the performance of our model using the Root Mean Squared Percent Error (RMSPE). Our analysis revealed significant correlations between specific viral mutations-S371F, T376A, D405N, and R408S-and increased transmission rates in all three countries. These mutations, prevalent in the Omicron BA.2 and BA.3 sublineages, are linked to immune evasion and heightened transmissibility.

Authors

  • Elham Shamsara
    Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad (FUM), Mashhad, Iran.
  • Florian König
    Methods in Medical Informatics, Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany.
  • Nico Pfeifer
    Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbrücken and Saarbrücken Graduate School of Computer Science, Saarland University, 66123 Saarbrücken.

Keywords

External Resources

Popular Topics
Recent Journals