Multipopulation mathematical modeling of vaccination campaign of COVID-19 in Cuba

Cuba is facing a severe COVID-19 outbreak despite its efforts to contain the virus and vaccinate its population. We present a computational analysis using a modified multi-population compartmental model (SIR) to simulate the pandemic in Cuba under different scenarios. Additionally, we develop and analyze multi-population SIRV (Susceptible, Infected, Recovered, Vaccinated) and SIQRDV (Susceptible, Infected, Quarantined, Recovered, Deceased, Vaccinated) models specific to the Cuban context. This study integrates real-world vaccination data, including locally developed vaccines, and considers regional differences in vaccination coverage and age-specific outcomes. Our model evaluates the impact of the vaccination program on infection rates, considering vaccine efficacy and coverage while comparing the outcomes of different measures on transmission dynamics. Results indicate that vaccination and non-pharmacologic interventions can significantly reduce infections and deaths.