Research On The Dynamic Model Of Epidemic Infectious Diseases Based On Human Mobility

The infectious disease model has always been the core of studying the spread of infectious diseases.The earliest mathematical model of infectious diseases based on modern mathematical models,that is,the classic warehouse model,was proposed by Kermack and Mc Kendrick in 1927.With the development of information science today,thanks to the explosive increase in the amount of data,the research on infectious disease modeling has been further developed.Due to the uniform mixing theory,the traditional warehouse model completely ignores the interaction mode between the individuals in the group,and cannot describe the true infectious disease diffusion process in combination with human mobility.At present,infectious disease models based on complex networks have received extensive attention from scholars,because they focus on complex interaction patterns between different individuals.The calculation results of the infectious disease model based on complex networks are more accurate,but the implementation process is more complicated.Complex network models are not suitable when modeling the infectious disease process on a large scale space.Based on the optimization of human mobility model,here proposes a modeling method for the dynamics of collective population infectious diseases.First,divide the target population into subpopulations on a large-scale space;Second,when describing human mobility,memory is introduced relative to the traditional first-order Markov model,and an adaptive memory mobility model is proposed to optimize mobility modeling among subpopulations;Third,combined with the SEIR infectious disease model to build a dynamic model of infectious diseases of the collective population to study the spread of infectious diseases in the target population;Finally,the proposed dynamic model is implemented based on the Python simulation platform,and combined with the current propagation data of the Coronavirus-19(COVID-19)in China's major provinces and cities for analysis and verification.The experimental results show that: the propagation process of the new coronavirus in China is simulated through experiments,and verifies that the dynamic model of infectious disease of aggregate population has high validity and accuracy.In addition,it is verified that the adaptive memory model has higher accuracy in describing human movement patterns than the first-order mobility model:(1)In terms of the scale of the outbreak and the prediction of the development trend of the epidemic,the dynamic model of infectious disease using an adaptive memory model is closer to the actual statistical data,mainly reflected in the statistics of the peak number of infections and the time point of peak;(2)In the hierarchical analysis of different administrative regions of the country,the experimental results of the infectious disease dynamics model using the adaptive memory model are closer to the actual statistical data,reflecting the universality of the optimized infectious disease dynamics model conclusions.