A1 Refereed original research article in a scientific journal
Fractional modelling of COVID-19 transmission incorporating asymptomatic and super-spreader individuals
Authors: Khalighi, Moein; Lahti, Leo; Ndaïrou, Faïçal; Rashkov, Peter; Torres, Delfim F. M.
Publisher: Elsevier Inc.
Publication year: 2025
Journal: Mathematical Biosciences
Journal name in source: Mathematical Biosciences
Article number: 109373
Volume: 380
ISSN: 0025-5564
eISSN: 1879-3134
DOI: https://doi.org/10.1016/j.mbs.2024.109373
Web address : https://doi.org/10.1016/j.mbs.2024.109373
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/477894399
The COVID-19 pandemic has presented unprecedented challenges worldwide, necessitating effective modelling approaches to understand and control its transmission dynamics. In this study, we propose a novel approach that integrates asymptomatic and super-spreader individuals in a single compartmental model. We highlight the advantages of utilizing incommensurate fractional order derivatives in ordinary differential equations, including increased flexibility in capturing disease dynamics and refined memory effects in the transmission process. We conduct a qualitative analysis of our proposed model, which involves determining the basic reproduction number and analysing the disease-free equilibrium’s stability. By fitting the proposed model with real data from Portugal and comparing it with existing models, we demonstrate that the incorporation of supplementary population classes and fractional derivatives significantly improves the model’s goodness of fit. Sensitivity analysis further provides valuable insights for designing effective strategies to mitigate the spread of the virus.
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Funding information in the publication:
This study has been supported by the Academy of Finland (330887 to MK, LL) and the UTUGS graduate school of the University of Turku (to MK). FN is supported by the Bulgarian Ministry of Education and Science, Scientific Programme ‘‘Enhancing the Research Capacity in Mathematical Sciences (PIKOM)’’, Contract No. DO1 67/05.05.2022. DFMT is supported by FCT (Fundação para a Ciência e a Tecnologia) through CIDMA projects UIDB/04106/2020 (https://doi. org/10.54499/UIDB/04106/2020) and UIDP/04106/2020 (https://doi.org/10.54499/UIDP/04106/2020), and the CoSysM3 project 2022.03091.PTDC (https://doi.org/10.54499/2022.03091.PTDC).
