Speaker
Description
Understanding the dynamics of infectious disease spread in interconnected populations is crucial for effective public health planning. In our increasingly mobile world, traditional modeling approaches often fall short in capturing the complexities of disease transmission across vast distances. This paper presents an innovative framework that combines the conceptual simplicity of ordinary differential equations (ODEs) with the predictive capabilities of computational models to analyze epidemic dynamics on interconnected networks. We derive scaling laws for epidemic propagation delays using perturbation expansion techniques, highlighting the influence of network topology and migration flux. Our findings reveal the diminishing returns of mobility reduction policies in mitigating epidemic spread, particularly after the outbreak has settled. This work contributes to a deeper understanding of the mechanisms driving infectious disease dynamics, providing quantitative insights for public health authorities to assess risks and optimize response strategies.
