Abstract: Objective To realize the visualization of the activity trajectory of infected persons by the activity trajectory information of infected persons in the major public health emergencies surveillance data. Methods Based on the method of deep mining of Neo4j graph database,the traffic survey data of major public health emergencies infected persons in outer suburb of Tianjin in early 2020 were used to implement data mining. Using text categorization and named entity recognition in the NLP deep learning technology,the traffic survey data were decomposed into six types of entities and their entity relationships,including infection,infection type,location,location type,activities and activities type,in order to track the movement track of the infected person and assist in delineating the risk area. Results In this study,the aggregation degree of 60 infected persons in any time period was selected,and finally 7 390 pieces of node and relationship data were formed（including 1 643 nodes and 5 747 relationships）,and the results were basically consistent with the actual clustering area. Furthermore,the data of epidemic situation in near suburb of Tianjin in 2022 were further verified. According to the data of epidemic survey,the activity locus and activity range of infected persons could be generated,and the activity heatmap of infected persons was basically consistent with the area sealed off by the government. Conclusion The distribution of the heat map of infected persons can be used to assess the possible spread of the epidemic situation and help to delineate the risk areas,which is beneficial to the further accurate epidemiological investigation and the precise control of the government.

Key words: Major public health emergencies, Neo4j diagram database, Trajectory tracking, Visual analysis