Modeling the effect of the vaccination campaign on the Covid-19 pandemic
This work addresses public health planning by modeling vaccination impacts on COVID-19 spread, but it is incremental as it builds on existing SIR models with new compartments and machine learning fitting.
The authors tackled forecasting the COVID-19 pandemic during vaccination campaigns by developing the SAIVR model, which extends the SIR model to include asymptomatic and vaccinated compartments, and found through simulations that herd immunity is not guaranteed under varying vaccine roll-out rates, efficacy, and hesitancy levels.
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.