The impact of human activity patterns on asymptomatic infectious processes in complex networks

Mingjie Li*, Mehmet A. Orgun, Jinghua Xiao, Weicai Zhong, Liyin Xue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The study of the impact of human activity patterns on network dynamics has attracted a lot of attention in recent years. However, individuals' knowledge of their own physical states has rarely been incorporated into modeling processes. In real life, for certain infectious processes, infected agents may not have any visible or physical signs and symptoms; therefore, they may believe that they are uninfected even when they have been infected asymptomatically. This infection awareness factor is covered neither in the classical epidemic models such as SIS nor in network propagation studies. In this article, we propose a novel infectious process model that differentiates between the infection awareness states and the physical states of individuals and extend the SIS model to deal with both asymptomatic infection characteristics and human activity patterns. With regards to the latter, we focus particularly on individuals' testing action, which is to determine whether an individual is infected by an epidemic. The simulation results show that less effort is required in controlling the disease when the transmission probability is either very small or large enough and that Poisson activity patterns are more effective than heavy-tailed patterns in controlling and eliminating asymptomatic infectious diseases due to the long-tail characteristic.

Original languageEnglish
Pages (from-to)3718-3728
Number of pages11
JournalPhysica A: Statistical Mechanics and its Applications
Volume391
Issue number14
DOIs
Publication statusPublished - 15 Jul 2012

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