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Epidemic spreading driven by L-step random walks with stochastic resetting
Mingyu Li1, Feng Zhu2, Xin Xiong1
1Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China.
Chaos (Woodbury, N.Y.)
|July 28, 2026
Summary
Recurrent mobility impacts disease spread. Stochastic resetting controls network topology, transitioning from global to local spread, thus managing outbreak size by balancing mobility range and confinement.
Area of Science:
- Epidemiology
- Network Science
- Statistical Physics
Background:
- Understanding disease transmission dynamics on complex networks is crucial.
- Recurrent mobility patterns significantly influence spatiotemporal epidemic spreading.
- Stochastic resetting offers a mechanism to modulate mobility and confinement.
Purpose of the Study:
- To characterize the spatiotemporal impact of recurrent mobility on disease spread.
- To investigate the role of stochastic resetting in modulating epidemic dynamics.
- To derive a mean-field threshold for outbreak prediction.
Main Methods:
- Susceptible-infected-recovered (SIR) model on complex networks.
- L-step random walks with stochastic resetting.
- Dynamic message passing framework and effective weighted directed networks.
Main Results:
- Resetting probability (γ) controls network topology from global to local.
- A continuous phase transition in outbreak size is induced by increasing γ.
- Phase diagrams quantify the trade-off between mobility range (L) and resetting probability (γ).
- Exposure localization explains the transition mechanism.
Conclusions:
- Mobility range and recurrent confinement present a quantifiable trade-off.
- Increasing resetting probability suppresses nonlocal exposure and reduces outbreak size.
- Higher mobility or infectiousness necessitates a greater critical resetting level to prevent epidemics.
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