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

TL;DR

This paper introduces SAIVR, a new mathematical model that forecasts Covid-19 epidemic trends during vaccination campaigns by integrating machine learning techniques to estimate parameters from real-world data.

Contribution

The study presents SAIVR, an extended SIR model incorporating asymptomatic and vaccinated compartments, combined with a semi-supervised machine learning approach for parameter estimation.

Findings

Model accurately fits infectious curves of 27 countries.

Varying vaccination rates significantly impact epidemic trajectories.

Herd immunity may be less achievable with more infectious variants.

Abstract

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.