Modeling the effect of the vaccination campaign on the COVID-19 pandemic

Population-wide vaccination is critical for containing the SARS-CoV-2 (COVID-19) pandemic when combined with restrictive and prevention measures. In this study we introduce SAIVR, a mathematical model able to forecast the COVID-19 epidemic evolution during the vaccination campaign. SAIVR extends the widely used Susceptible-Infectious-Removed (SIR) model by considering the Asymptomatic (A) and Vaccinated (V) compartments. The model contains several parameters and initial conditions that are estimated by employing a semi-supervised machine learning procedure. After training an unsupervised neural network to solve the SAIVR differential equations, a supervised framework then estimates the optimal conditions and parameters that best fit recent infectious curves of 27 countries. Instructed by these results, we performed an extensive study on the temporal evolution of the pandemic under varying values of roll-out daily rates, vaccine efficacy, and a broad range of societal vaccine hesitancy/denial levels. The concept of herd immunity is questioned by studying future scenarios which involve different vaccination efforts and more infectious COVID-19 variants. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

In this study, a mathematical model called SAIVR is introduced to forecast the evolution of the COVID-19 epidemic during the vaccination campaign. The model extends the widely used SIR model by considering asymptomatic and vaccinated individuals, and employs a semi-supervised machine learning procedure to estimate parameters and initial conditions based on infectious curves of 27 countries.