Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks.

Navavat Pipatsart,Wannapong Triampo,Charin Modchang

doi:10.1155/2017/2403851

Abstract

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.

Highlights

Within few decades, numerous studies on infectious disease transmission using the network theory have been carried out
The infectious fraction tended to be decreased in case of the static network model
Thereafter, we studied the infectious disease transmission in another adaptive network model, namely, the type II adaptive random network, by using the equivalent conditions as in the previous section to investigate the effect of network adaptation dynamics on the infectious disease transmission

Summary

Introduction

Numerous studies on infectious disease transmission using the network theory have been carried out. Network models tend to be very powerful tools that provide understanding of the disease transmission in human populations and allow the assumptions of either social or sexual contacts [6]. The vast majority of infectious disease transmission models on networks employed static networks [7]. There are studies that implemented rules on dynamical network structures that opened the possibility of network adaptation. These rules help to generate complex network models and are expected to reflect some realworld networks [8,9,10,11,12,13]

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Results

Conclusion