Epicenters are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected . Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics . This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control . By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model . With this model, we revealed insight into how COVID-19 was spread from the epicenter Wuhan to other regions in Mainland China based on the large population flow network data . By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicenter . In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicenter Wuhan to other regions of China, and the subsequent control of local epidemics . These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.