Sensitivity analysis of disease-information coupling propagation dynamics model parameters

Abstract

The disease-information coupling propagation dynamics model is a widely used model for studying the spread of infectious diseases in society, but the parameter settings and sensitivity are often overlooked, which leads to enlarged errors in the results. Exploring the influencing factors of the disease-information coupling propagation dynamics model and identifying the key parameters of the model will help us better understand its coupling mechanism and make accurate recommendations for controlling the spread of disease. In this paper, Sobol global sensitivity analysis algorithm is adopted to conduct global sensitivity analysis on 6 input parameters (different cross regional jump probabilities, information dissemination rate, information recovery rate, epidemic transmission rate, epidemic recovery rate, and the probability of taking preventive actions) of the disease-information coupling model with the same interaction radius and heterogeneous interaction radius. The results show that: (1) In the coupling model with the same interaction radius, the parameters that have the most obvious influence on the peak density of nodes in stateAIand the information dissemination scale of the information are the information dissemination rateβIand the information recovery rateμI. In the coupling model of heterogeneous interaction radius, the parameters that have the most obvious impact on the peak density of nodes in theAIstate of the information layer are: information spread rateβI, disease recovery rateμE, and the parameter that has a significant impact on the scale of information spread is the information spread rateβIand information recovery rateμI. (2) Under the same interaction radius and heterogeneous interaction radius, the parameters that have the most obvious influence on peak density of nodes in stateSEand the disease transmission scale of the disease layer are the disease transmission rateβE, the disease recovery rateμE, and the probability of an individual moving across regionspjump.