Dynamic evolution analysis and parameter optimization design of data-driven network infectious disease model

Abstract

Background and Objective:

globalization and population mobility have increased the spread of infectious diseases and challenged public health security. This paper proposes a complex network epidemic model with nonlinear incidence rate and quadratic transmission. The Turing pattern, sensitivity analysis and parameter identification of the epidemic model under different network structures are studied;

Methods:

this paper discusses the Turing pattern of the model under different network structures, and identifies the key parameters of the model through sensitivity analysis. The influence of network dimension on the spread of infectious diseases on random networks is also explored, and the problems of minimum path and minimum cover set of random networks are further discussed. We also carry out parameter identification experiments, adopt gradient descent algorithm to realize heterogeneous spatial fitting pattern of red blood cell plasma and simulate the transmission path of $COVID$-19 through Markov chain Monte Carlo fitting experiment, verifying the effectiveness of the model;

Results:

the necessary conditions for Turing instability on homogeneous and heterogeneous networks are found. On the heterogeneous lattice network, we observe the special patterns of equal density population. Sensitivity analysis shows that the higher the infection rate, the more infected people. On random networks, the higher the dimension, the better the effect of suppressing the spread of infectious diseases. Through comparison experiment, it is found that gradient descent algorithm has the best performance in parameter identification experiments. Red blood cell plasma fitting experiment reveals the spatial density distribution of infection rate;

Conclusions:

this study provides theoretical support for the prevention and control of infectious diseases, and the complex network model can simulate the transmission process of infectious diseases more accurately. Sensitivity analysis and parameter identification experiments reveal the key influencing factors of propagation and the role of network structure. The effectiveness of the model is supported by actual data, which is helpful for the government health departments to formulate scientific prevention and control strategies.