Quantifying social interventions in combating COVID-19 via a symmetry-based model

Abstract The COVID-19 pandemic reveals new features of substantial changes in rates of infection, cure, and death, resulting from social intervention, which significantly challenges traditional SEIR-type models. This paper develops a symmetry-based model for quantifying social interventions in combating COVID-19. We find three key order parameters, separating degree (S) for susceptible populations, healing degree (H) for mild cases, and rescuing degree (R) for server cases, all display logistic dynamics, which establish a novel dynamic model named SHR. Furthermore, we discover two evolutionary patterns of healing degree with a universal power law in 23 areas in the first wave. Remarkably, the model yields a quantitative evaluation of the ‘dynamic back-to-zero’ policy in the third wave in Beijing by 12 datasets of different sizes. In conclusion, the SHR model constitutes a rational basis to understand this complex epidemic, and for policymakers to carry out sustainable anti-epidemic measures to minimize its impact.