MURAL - Maynooth University Research Archive Library



    Optimal age-specific vaccination control for COVID-19: An Irish case study


    Naz, Rehana, Zavrakli, Eleni, Parnell, Andrew, Malone, David, Duffy, Ken and Dey, Subhrakanti (2023) Optimal age-specific vaccination control for COVID-19: An Irish case study. PLOS ONE, 18 (9). e0290974. ISSN 1932-6203

    [thumbnail of AndrewParnellOptimal2023.pdf]
    Preview
    Text
    AndrewParnellOptimal2023.pdf

    Download (4MB) | Preview

    Abstract

    The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland’s COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use.
    Item Type: Article
    Keywords: Age groups; Analysis; Biology and Life Sciences; Case reports; Case studies; China; Control theory; Coronaviruses; COVID-19; COVID-19 vaccines;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 18800
    Identification Number: 10.1371/journal.pone.0290974
    Depositing User: Andrew Parnell
    Date Deposited: 22 Aug 2024 13:17
    Journal or Publication Title: PLOS ONE
    Publisher: Public Library of Science
    Refereed: Yes
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/18800
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

    Repository Staff Only (login required)

    Item control page
    Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads