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Impact of optimal vaccination and social distancing on COVID-19 pandemic

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  • Saha, Sangeeta
  • Samanta, Guruprasad
  • Nieto, Juan J.

Abstract

The first COVID-19 case was reported at Wuhan in China at the end of December 2019 but till today the virus has caused millions of deaths worldwide. Governments of each country, observing the severity, took non-pharmaceutical interventions from the very beginning to break the chain of higher transmission. Fortunately, vaccines are available now in most countries and people are asked to take recommended vaccines as precautionary measures. In this work, an epidemiological model on COVID-19 is proposed where people from the susceptible and asymptomatically infected phase move to the vaccinated class after a full two-dose vaccination. The overall analysis says that the disease transmission rate from symptomatically infected people is most sensitive on the disease prevalence. Moreover, better disease control can be achieved by vaccination of the susceptible class. In the later part of the work, a corresponding optimal control problem is considered where maintaining social distancing and vaccination procedure change with time. The result says that even in absence of social distancing, only the vaccination to people can significantly reduce the overall infected population. From the analysis, it is observed that maintaining physical distancing and taking vaccines at an early stage decreases the infection level significantly in the environment by reducing the probability of becoming infected.

Suggested Citation

  • Saha, Sangeeta & Samanta, Guruprasad & Nieto, Juan J., 2022. "Impact of optimal vaccination and social distancing on COVID-19 pandemic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 285-314.
    • Handle: RePEc:eee:matcom:v:200:y:2022:i:c:p:285-314
    DOI: 10.1016/j.matcom.2022.04.025
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    References listed on IDEAS

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    1. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    Cited by:

    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Ahmed, Marzia & Sulaiman, Mohd Herwan & Mohamad, Ahmad Johari & Rahman, Mostafijur, 2024. "Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 218(C), pages 248-265.
    3. Jang, Gyeong Hwan & Kim, Sung Jin & Lee, Mi Jin & Son, Seung-Woo, 2024. "Effectiveness of vaccination and quarantine policies to curb the spread of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    4. Li, Tingting & Guo, Youming, 2022. "Optimal control and cost-effectiveness analysis of a new COVID-19 model for Omicron strain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Haque, Mainul & Basir, Fahad Al & Venturino, Ezio & Saeed, Abdulhalim & Smith?, Stacey R., 2023. "Mathematical modelling of clonorchiasis with human treatment and fish vaccination versus snail control," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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