A ripple-spreading network model for the study of infectious disease transmission

• Published in: 2012 5th International Conference on Biomedical Engineering and Informatics pp. 1004 - 1010
• Main Authors: Liao, Jian-Qin, Hu, Xiao-Bing, Wang, Ming, Leeson, Mark S., Hines, Evor L., Di Paolo, Ezequiel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Analytical models; Biological system modeling; Computational modeling; epidemic model; Epidemics; genetic algorithm; Infectious diseases; Mathematical model; parameter optimization; ripple-spreading network; stochastic process
• ISBN: 9781467311830; 1467311839
• DOI: 10.1109/BMEI.2012.6513120

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential of capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process can well simulate the effect of random contacts and movements of individuals on the probability of infection, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals' physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and polices, and it is highly flexible to modifications. Genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the propose method is illustrated by a preliminary study.