Trend Analysis of COVID-19 Based on Network Topology Description

In this study, the trend of the epidemic situation of COVID-19 is analyzed based on the analysis method for network topology. Combining with the sliding window method, the dynamic networks with different topologies for each window are built to reflect the relationship of the data on different days. Then, the static statistical features on network topologies at different times are extracted during the dynamic evolution of complex networks. A new trend function defined on the average degree and clustering coefficient of the network is tailored to measure the characteristics of the trend. Through the value of the trend function, we can analyze the trend of the epidemic situation in real time. It is found that if the value of the trend function tends to decrease, it means that the epidemic will have to be effectively controlled. Finally, we put forward some suggestions for early control of the epidemic.