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

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  • Angeli, Mattia
  • Neofotistos, Georgios
  • Mattheakis, Marios
  • Kaxiras, Efthimios

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.

Suggested Citation

  • Angeli, Mattia & Neofotistos, Georgios & Mattheakis, Marios & Kaxiras, Efthimios, 2022. "Modeling the effect of the vaccination campaign on the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    • Handle: RePEc:eee:chsofr:v:154:y:2022:i:c:s0960077921009759
    DOI: 10.1016/j.chaos.2021.111621
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    References listed on IDEAS

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    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Mingxin Zhang & Alexander Verbraeck & Rongqing Meng & Bin Chen & Xiaogang Qiu, 2016. "Modeling Spatial Contacts for Epidemic Prediction in a Large-Scale Artificial City," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-3.
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    4. Пигнастый, Олег & Koжевников, Георгий, 2019. "Распределенная Динамическая Pde-Модель Программного Управления Загрузкой Технологического Оборудования Производственной Линии [Distributed dynamic PDE-model of a program control by utilization of t," MPRA Paper 93278, University Library of Munich, Germany, revised 02 Feb 2019.
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    Cited by:

    1. Wang, Chuanbiao & Liu, Ruiying & Wang, Yan, 2023. "The spread dynamics model of the interaction between rumors and derivative rumors in emergencies under the control strategy," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
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    4. Cai, Junyang & Zhou, Jian, 2022. "How many asymptomatic cases were unconfirmed in the US COVID-19 pandemic? The evidence from a serological survey," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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