Urban epidemic situation space-time prediction method and system, terminal and storage medium

Abstract

The invention relates to an urban epidemic situation space-time prediction method and system, a terminal and a storage medium. The method comprises the following steps: collecting individual movement track data and infectious disease case data in a city; processing the individual movement track data, extracting population movement traffic among the regions, dividing the population movement traffic according to time attributes, and obtaining population movement relationships under the time attributes based on proximity relationships of the regions; calculating the similarity of the infectious disease case data among the areas by using a similarity algorithm based on a histogram, and obtaining the position attention relationship of the areas according to the similarity of the infectious disease case data; and constructing an infectious disease space-time prediction model based on the graph neural network and the long-short-term memory network according to the population movement relationship, the proximity relationship and the position attention relationship. According to the invention, modeling can be carried out on time dependence and space dependence in infectious disease case data, and the space perception capability and prediction performance of a space-time prediction model are improved.

Description

technical field [0001] The application belongs to the technical field of infectious disease computing, and in particular relates to a method, system, terminal and storage medium for temporal and spatial prediction of urban epidemics. Background technique [0002] Infectious disease forecasting has gained prominence in recent years due to the increased availability of public health surveillance data and the development of sophisticated methods. Real-time prediction of disease infections remains hampered by a lack of current estimates of mobility and interaction patterns. Mobility and interaction patterns are key drivers of disease transmission. Prediction of the incidence trend of infectious diseases is an important issue in the field of public health. Timely discovery, tracking and prediction of key information of infectious diseases, such as peak intensity and outbreak time, are crucial for effective formulation of prevention and control strategies and implementation of int...

AI Technical Summary

Problems solved by technology

However, most of these studies did not take into account the spatial interaction caused by population movement and the spatio-temporal diffusion pattern of infectious diseases within cities when modeling the spatial dependencies between different regions. Accurately predict the epidemic trend of infectious diseases

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