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Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model

Author

Listed:
  • Lei Zhang

    (Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China)

  • Guang-Hui She

    (Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China)

  • Yu-Rong She

    (Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China)

  • Rong Li

    (Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
    State Key Laboratory for Turbulence & Complex Systems, Peking University, Beijing 100871, China)

  • Zhen-Su She

    (Institute of Health Systems Engineering, College of Engineering, Peking University, Beijing 100871, China
    State Key Laboratory for Turbulence & Complex Systems, Peking University, Beijing 100871, China)

Abstract

The COVID-19 pandemic has revealed new features in terms of substantial changes in rates of infection, cure, and death as a result of social interventions, which significantly challenges traditional SEIR-type models. In this paper we developed a symmetry-based model for quantifying social interventions for combating COVID-19. We found that three key order parameters, separating degree ( S ) for susceptible populations, healing degree ( H ) for mild cases, and rescuing degree ( R ) for severe cases, all display logistic dynamics, establishing a novel dynamic model named SHR . Furthermore, we discovered two evolutionary patterns of healing degree with a universal power law in 23 areas in the first wave. Remarkably, the model yielded a quantitative evaluation of the dynamic back-to-zero policy in the third wave in Beijing using 12 datasets of different sizes. In conclusion, the SHR model constitutes a rational basis by which we can understand this complex epidemic and policymakers can carry out sustainable anti-epidemic measures to minimize its impact.

Suggested Citation

  • Lei Zhang & Guang-Hui She & Yu-Rong She & Rong Li & Zhen-Su She, 2022. "Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:476-:d:1017375
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    References listed on IDEAS

    as
    1. Wenning Li & Jianhua Gong & Jieping Zhou & Hongkui Fan & Cheng Qin & Yujiang Gong & Weidong Hu, 2022. "The Analysis of Patterns of Two COVID-19 Outbreak Clusters in China," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    2. Alberto Godio & Francesca Pace & Andrea Vergnano, 2020. "SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    3. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    4. Consolini, Giuseppe & Materassi, Massimo, 2020. "A stretched logistic equation for pandemic spreading," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    Full references (including those not matched with items on IDEAS)

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