Speaker
Description
Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on its development trends and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this research constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, the CEMM model is developed from an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. Through scenario simulation, this study also quantitatively evaluated the effect of such control measures as “city lockdown” and “decreasing population mobility” on containing the spatial spread of the COVID-19 epidemic in China.
