2006
DOI: 10.1103/physreve.73.046131
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Epidemic variability in complex networks

Abstract: We study numerically the variability of the outbreak of diseases on complex networks. We use a susceptible-infected model to simulate the disease spreading at short times in homogeneous and in scale-free networks. In both cases, we study the effect of initial conditions on the epidemic dynamics and its variability. The results display a time regime during which the prevalence exhibits a large sensitivity to noise. We also investigate the dependence of the infection time of a node on its degree and its distance… Show more

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Cited by 55 publications
(60 citation statements)
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“…In most cases our theoretical predictions are in a good agreement with the numerical effective epidemic thresholds identified by the variability measure [28,29], which has been confirmed to be effective for identifying the SIR effective epidemic threshold [30]. Although there exist some differences between the theoretical predictions and numerical results in networks with disassortative mixing, the theoretical effective epidemic threshold displays the same trend to that of the numerical effective epidemic threshold.…”
Section: Introductionsupporting
confidence: 72%
See 1 more Smart Citation
“…In most cases our theoretical predictions are in a good agreement with the numerical effective epidemic thresholds identified by the variability measure [28,29], which has been confirmed to be effective for identifying the SIR effective epidemic threshold [30]. Although there exist some differences between the theoretical predictions and numerical results in networks with disassortative mixing, the theoretical effective epidemic threshold displays the same trend to that of the numerical effective epidemic threshold.…”
Section: Introductionsupporting
confidence: 72%
“…To numerically identify the effective epidemic threshold of the SIR model, we use the variability measure [28,29] …”
Section: Resultsmentioning
confidence: 99%
“…Both Monte Carlo simulations [14][15][16][17] and theoretical study [18] have investigated the effects of network structures on epidemic spreading velocity [19,20], epidemic variability [21,22], epidemic size [23][24][25][26][27][28], and epidemic thresholds [29][30][31][32][33][34]. Both the epidemic size and threshold can indicate the probability of an epidemic occurring [32], which seeds are influential [35][36][37][38], and how to effectively control the epidemic once it begins [39][40][41].…”
Section: Introductionmentioning
confidence: 99%
“…For simplicity, estimates of R 0 for infectious diseases have mostly been based on either susceptible-infectious (SI) or susceptible-infectious-recovered (SIR) models assuming a homogeneous mixing process. Recognising that these conditions are rarely met, attempts have been made to quantify the extent and direction of the bias in estimates of R 0 in disease epidemics in animal and human populations [15,23,43,50,57]. In this study, we have extended the approach of May et al [50] by introducing stochastic variation in the number of contacts and duration of infectiousness (Eq.…”
Section: Discussionmentioning
confidence: 99%