Angeli Mattia, Neofotistos Georgios, Mattheakis Marios, Kaxiras Efthimios
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
Department of Physics, Harvard University, Cambridge, MA 02138, USA.
Chaos Solitons Fractals. 2022 Jan;154:111621. doi: 10.1016/j.chaos.2021.111621. Epub 2021 Nov 19.
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.
在与限制和预防措施相结合时，全人群疫苗接种对于遏制严重急性呼吸综合征冠状病毒2（SARS-CoV-2，即新冠病毒）大流行至关重要。在本研究中，我们引入了SAIVR，这是一个能够预测疫苗接种运动期间新冠疫情演变的数学模型。SAIVR通过考虑无症状（A）和接种疫苗（V）人群，扩展了广泛使用的易感-感染-康复（SIR）模型。该模型包含几个参数和初始条件，这些参数和初始条件通过采用半监督机器学习程序进行估计。在训练一个无监督神经网络以求解SAIVR微分方程之后，一个有监督框架随后估计最符合27个国家近期感染曲线的最优条件和参数。受这些结果的指导，我们针对不同的每日疫苗接种率、疫苗效力以及广泛的社会疫苗犹豫/拒绝水平值，对大流行的时间演变进行了广泛研究。通过研究涉及不同疫苗接种努力和更具传染性的新冠病毒变体的未来情景，群体免疫的概念受到了质疑。
