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

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.