Spreading dynamics following bursty human activity patterns

摘要: We study the susceptible-infected model with power-law waiting time distributions $P(\ensuremath{\tau})~{\ensuremath{\tau}}^{\ensuremath{-}\ensuremath{\alpha}}$, as a of spreading dynamics under heterogeneous human activity patterns. found that average number new infections $n(t)$ at $t$ decays power law in long-time limit, $n(t)~{t}^{\ensuremath{-}\ensuremath{\beta}}$, leading to extremely slow prevalence decay. also exponent $\ensuremath{\beta}$ is related distribution $\ensuremath{\alpha}$ way depending on interactions between agents but insensitive network topology. These observations are well supported by both theoretical predictions and long decay real social phenomena. Our results unify individual patterns macroscopic collective level.