A deterministic model of COVID-19 with differential infectivity and vaccination booster

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dc.contributor.author Tchoumi, Stephane Yanick
dc.contributor.author Schwartz, Elissa J.
dc.contributor.author Tchuenche, Jean M.
dc.date.accessioned 2024-05-15T11:43:42Z
dc.date.available 2024-05-15T11:43:42Z
dc.date.issued 2024-03
dc.description DATA AVILABILITY : Data will be made available on request. en_US
dc.description.abstract Vaccine boosters have been recommended to mitigate the spread of the coronavirus disease 2019 (COVID-19) pandemic. A mathematical model with three vaccine doses and susceptibility is formulated. The model is calibrated using the cumulative number of hospitalized cases from Alberta, Canada. Estimated values from the fitting are used to explore the potential impact of the booster doses to mitigate the spread of COVID-19. Sensitivity analysis on initial disease transmission shows that the most sensitive parameters are the contact rate, the vaccine efficacy, the proportion of exposed individuals moving into the symptomatic and asymptomatic classes, and the recovery rate from asymptomatic infection. Simulation results support the positive populationlevel impact of the second and third COVID-19 vaccine boosters to reduce the number of infections and hospitalizations. Public health policy and decision-makers should continue advocating and encouraging people to get booster doses. As the end of the pandemic is in sight, there should be no complacency before it resolves. en_US
dc.description.department Mathematics and Applied Mathematics en_US
dc.description.librarian am2024 en_US
dc.description.sdg SDG-03:Good heatlh and well-being en_US
dc.description.sponsorship The DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering at the University of Pretoria. en_US
dc.description.uri https://www.elsevier.com/locate/dajour en_US
dc.identifier.citation Tchoumi, S.Y., Schwartz, E.J., Tchuenche, J.M. 2024, 'A deterministic model of COVID-19 with differential infectivity and vaccination booster', Decision Analytics Journal, vol. 10, art. 100374, pp. 1-11. https://DOI.org/10.1016/j.dajour.2023.100374. en_US
dc.identifier.issn 2772-6622
dc.identifier.other 10.1016/j.dajour.2023.100374
dc.identifier.uri http://hdl.handle.net/2263/95992
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license. en_US
dc.subject Non-linear mathematical model en_US
dc.subject Simulation en_US
dc.subject Vaccination en_US
dc.subject Basic reproduction number en_US
dc.subject Sensitivity analysis en_US
dc.subject COVID-19 pandemic en_US
dc.subject Coronavirus disease 2019 (COVID-19) en_US
dc.subject SDG-03: Good health and well-being en_US
dc.title A deterministic model of COVID-19 with differential infectivity and vaccination booster en_US
dc.type Article en_US


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