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Mathematical Problems in Engineering
Volume 2013, Article ID 506240, 11 pages
http://dx.doi.org/10.1155/2013/506240
Research Article

Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm

1State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2Angel Women’s & Children’s Hospital, Chengdu 610041, China
3School of Engineering, University of Warwick, Coventry CV4 7AL, UK

Received 20 July 2013; Revised 8 September 2013; Accepted 8 September 2013

Academic Editor: J. A. Tenreiro Machado

Copyright © 2013 Jian-Qin Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, 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 policies, and it is highly flexible to modifications. A 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 proposed method is illustrated by simulation results.