Spatial Data Analysis of Sudden Epidemic Diseases Based on Case-Based Travel Data

Abstract

In recent years, health geography has attracted the attention of more and more institutions and scholars in China, but as far as the results of a large number of studies are concerned, there are not many related types of research. Geographic Information System (GIS) and spatial data method can effectively deal with complex spatial data, time data and attribute data in diseases, analyze the relationship between the distribution and changes of diseases in different regions, and explore the causes of diseases and various environmental factors. According to the analysis results, the hierarchical model of spatial aggregation can find out the dynamic spatial distribution law of epidemic occurrence and prevalence more intuitively, and carry out epidemic detection and early warning in depth, thus laying the foundation for formulating appropriate prevention and control strategies. Corona Virus Disease 2019 is highly contagious, with diverse and complicated transmission methods, wide spread and high incidence. Therefore, taking Corona Virus Disease 2019 as an example, it is of great significance to study the application of spatial aggregation hierarchical model and spatial data analysis technology in epidemiology through GIS technology, especially in the spatial aggregation hierarchical model to visualize the travel data of epidemic cases and show the temporal and spatial distribution characteristics. In this work, Kriging interpolation will be tried to supplement the travel data of cases and then analyze the spatial data.