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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 2403851, 11 pages
Research Article

Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

1Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
2Centre of Excellence in Mathematics, CHE, Bangkok 10400, Thailand
3Thailand Center of Excellence in Physics, CHE, 328 Si Ayutthaya Road, Bangkok 10400, Thailand

Correspondence should be addressed to Charin Modchang; moc.liamg@gnahcdomc

Received 14 March 2017; Accepted 15 August 2017; Published 17 September 2017

Academic Editor: Delfim F. M. Torres

Copyright © 2017 Navavat Pipatsart 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.


We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.