Modeling of epidemic spreading on multilayer networks in uncertain environments. (22nd October 2019) - Jiang, Jian; Liang, Junhao; Zhou, Tianshou
Abstract: Epidemic spreading on networks involves many factors such as the interaction between subpopulations, resource amount invested for control and noise due to environmental uncertainty. In order to model the effects of these factors, we consider a toy model of epidemic spreading, where the noise may be so strong that small-noise theory fails. We use the binomial moment approach to carry out stochastic analysis. As a result, we find that there are respective critical thresholds of resource amount and internal noise, such that a significant fraction of the total population may be infected if the resource amount or the noise intensity is beyond its threshold and the outbreak may be effectively eradicated otherwise. In contrast to the role of internal noise, external noise always decreases the fraction of the infected population. These results could have important implications for government decisions on the control of epidemic spreading.