Non-Markovian epidemic spreading on temporal networks
arXiv:2303.04740 · doi:10.1016/j.chaos.2023.113664
Abstract
Many empirical studies have revealed that the occurrences of contacts associated with human activities are non-Markovian temporal processes with a heavy tailed inter-event time distribution. Besides, there has been increasing empirical evidence that the infection and recovery rates are time-dependent. However, we lack a comprehensive framework to analyze and understand non-Markovian contact and spreading processes on temporal networks. In this paper, we propose a general formalism to study non-Markovian dynamics on non-Markovian temporal networks. We find that, under certain conditions, non-Markovian dynamics on temporal networks are equivalent to Markovian dynamics on static networks. Interestingly, this result is independent of the underlying network topology.
References in corpus (21)
- Epidemic processes in complex networks
- Temporal Networks
- Activity driven modeling of time varying networks
- Modern temporal network theory: A colloquium
- Universal behavior in a generalized model of contagion
- A generalized model of social and biological contagion
- Time varying networks and the weakness of strong ties
- Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks
- Nature of the epidemic threshold for the susceptible-infected-susceptible dynamics in networks
- Simulating non-Markovian stochastic processes
- Temporal network structures controlling disease spreading
- Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes
- Contrasting Effects of Strong Ties on SIR and SIS Processes in Temporal Networks
- Bass-SIR model for diffusion of new products
- Percolation in self-similar networks
- Non-Markovian recovery makes complex networks more resilient against large-scale failures
- Non-Markovian SIR epidemic spreading model
- Modeling temporal networks using random itineraries
- Burstiness in activity-driven networks and the epidemic threshold
- Memory-induced complex contagion in epidemic spreading
- Unifying continuous, discrete, and hybrid susceptible-infected-recovered processes on networks