Resilience analysis for confronting the spreading risk of contagious diseases

Spreading risks of contagious diseases have threatened human lives and disrupted the global economy. Protecting humanity from contagious diseases, such as coronavirus, is a critical task of our time. Unlike other types of risks, the spreading of contagious diseases could be enhanced by human movements. As human movements are inherently dynamic and heterogenous, it brings challenges in analyzing the spreading on a large scale. Here, we combine network theory and epidemiology models to construct a SIRN model for capturing spatiotemporal characteristics of spreading risks. With mean-field theory and Lyapunov theorem, our model reveals the relation between spreading risk and the network topology of human movement. The model allows us to construct a resilient strategy to control and contain the risk. The approach is tested retrospectively with coronavirus cases in China and the United States. Our results adequately interpret the historical trends and justify the resilience of nations' strategies. The model demonstrates a pattern of resilience in dynamic networks, which could shed light on handling contagious risks.

Automated summary

The spread of contagious diseases, such as coronavirus, poses a threat to human lives and the global economy. We have developed a SIRN model using network theory and epidemiology models to capture the spatiotemporal characteristics of spreading risks. By analyzing the relation between spreading risk and the network topology of human movement, our model allows us to construct a resilient strategy to control and contain the risk.