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

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• COVID-19 pandemic presented unique challenges to traditional epidemiological models like SEIR.
• Social interventions significantly altered infection, cure, and death rates, necessitating new modeling approaches.

Purpose of the Study:

• To develop a novel, symmetry-based dynamic model for quantifying the impact of social interventions during the COVID-19 pandemic.
• To establish a framework for understanding epidemic dynamics and informing sustainable public health policies.

Main Methods:

• Developed a novel dynamic model, termed the SHR model, based on symmetry principles.
• Identified three key order parameters: separating degree (S) for susceptible populations, healing degree (H) for mild cases, and rescuing degree (R) for severe cases.
• Analyzed the logistic dynamics of these parameters and investigated evolutionary patterns of the healing degree using a universal power law.

Main Results:

• The SHR model demonstrated that susceptible, healing, and rescuing population degrees follow logistic dynamics.
• Identified two distinct evolutionary patterns of the healing degree, exhibiting a universal power law across 23 regions during the first wave.
• Successfully applied the SHR model to quantitatively evaluate Beijing's dynamic 'zero-COVID' policy during the third wave using diverse datasets.

Conclusions:

• The SHR model provides a robust framework for understanding complex epidemic dynamics, particularly in response to social interventions.
• The model's findings support the development of rational, sustainable anti-epidemic strategies for minimizing the impact of infectious diseases.
• The quantitative evaluation of public health policies using the SHR model can aid policymakers in effective decision-making.