The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population.
In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct).
Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low.

To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer’s vaccine confirming the results of clinical tests.

Extended version of the paper published in Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE)

How to cite: Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. “Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration.” In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (pp. 990-995). IEEE. DOI: https://doi.org/10.1109/CASE49439.2021.9551418