Structural Characterization of Horizontal Visibility Network Based on Time Series of Emergency Disease Visits

Abstract

The conversion of time series into visualized networks is one of the most important tools for comprehending data patterns and trends. Here, we initially translated hospital emergency data into complex networks through horizontal visualization techniques. Subsequently, we carefully analyzed the topologies of the network structures corresponding to each category of disease by applying the structural statistics approach of network science. Our investigation unveiled that networks constructed by this method exhibit larger clustering coefficients and shorter average shortest paths of the networks, leading to a stronger small-world effect. Moreover, we observed that the average degree of the networks is notably higher for the respiratory diseases, which may be caused by the heightened contagious diseases in the respiratory diseases. Further, by calculating the maximum eigenvalue of the network Laplace matrix, we found that the maximum eigenvalue of the respiratory diseases are generally higher than other categories of diseases. This provides a crucial analytical tool for the proactive prevention of certain specific diseases and more effective response strategies during emergency triage protocols.