Abstract
We study numerically how the structures of distinct networks influence the epidemic dynamics in contact process. We first find that the variability difference between homogeneous and heterogeneous networks is very narrow, although the heterogeneous structures can induce the lighter prevalence. Contrary to non-community networks, strong community structures can cause the secondary outbreak of prevalence and two peaks of variability appeared. Especially in the local community, the extraordinarily large variability in early stage of the outbreak makes the prediction of epidemic spreading hard. Importantly, the bridgeness plays a significant role in the predictability, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is. Also, we investigate the effect of different disease reaction mechanisms on variability, and find that the different reaction mechanisms will result in the distinct variabilities at the end of epidemic spreading.
Highlights
Threshold when the population size is infinite and the exponent of degree distribution c 3.7–11 With the further study, the local structures of complex networks bring quantitative influences on epidemic spreading
By investigating the variabilities of contact process in distinct networks, we show numerically that the bridgeness plays a significant role on the predictability of the epidemic pattern in community network, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is
Crepey et al have found that initial conditions such as the degree heterogeneity of the seed show a large variability on the prediction of the epidemic prevalence, and the infection time of nodes have non-negligible fluctuations caused by the further distance and the multiplicity of paths to the seed
Summary
Threshold when the population size is infinite and the exponent of degree distribution c 3.7–11 With the further study, the local structures of complex networks (such as degree correlation, clustering coefficient, and community structure) bring quantitative influences on epidemic spreading.. Considering the complicated local structures in real networks, the forecasting capabilities (i.e., variability) of current numerical models have been investigated.. Considering the complicated local structures in real networks, the forecasting capabilities (i.e., variability) of current numerical models have been investigated.15 Both the stochastic nature of travel flows, and initial conditions can affect the reliability of the epidemic spreading forecast.. Predictability of the model is totally overlooked To this end, we study how the structures of distinct networks (i.e., homogeneous, heterogeneous, and community networks) influence the variabilities of epidemic patterns in CP.
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