Sensitivity analysis of disease-information coupling propagation dynamics model parameters

• Published in: PloS one Vol. 17; no. 3; p. e0265273
• Main Authors: Yang, Yang, Liu, Haiyan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.03.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Care and treatment; Communicable diseases; Communicable Diseases - epidemiology; Computer and Information Sciences; Coupling; Density; Diagnosis; Disease control; Disease transmission; Dynamics; Epidemics; Humans; Infectious diseases; Information Dissemination; Interaction parameters; Mathematical models; Medicine and Health Sciences; Methods; Modelling; Nodes; Parameter identification; Parameter sensitivity; Physical Sciences; Prevention; Probability; Propagation; Quantitative analysis; Random variables; Recovery; Research and Analysis Methods; Risk factors; Sensitivity analysis; Stochastic models; China; Beijing China
• DOI: 10.1371/journal.pone.0265273
• ISSN: 1932-6203

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 state A I and the information dissemination scale of the information are the information dissemination rate β I and the information recovery rate μ I . In the coupling model of heterogeneous interaction radius, the parameters that have the most obvious impact on the peak density of nodes in the A I state of the information layer are: information spread rate β I , disease recovery rate μ E , and the parameter that has a significant impact on the scale of information spread is the information spread rate β I and information recovery rate μ I . (2) Under the same interaction radius and heterogeneous interaction radius, the parameters that have the most obvious influence on peak density of nodes in state S E and the disease transmission scale of the disease layer are the disease transmission rate β E , the disease recovery rate μ E , and the probability of an individual moving across regions p jump .