SIS Epidemic Modelling on Homogeneous Networked System: General Recovering Process and Mean-Field Perspective
arXiv:2505.12290 · doi:10.1016/j.apm.2025.116188
Abstract
Although we have made progress in understanding disease spread in complex systems with non-Poissonian activity patterns, current models still fail to capture the full range of recovery time distributions. In this paper, we propose an extension of the classic susceptible-infected-susceptible (SIS) model, called the general recovering process SIS (grp-SIS) model. This model incorporates arbitrary recovery time distributions for infected nodes within the system. We derive the mean-field equations assuming a homogeneous network, provide solutions for specific recovery time distributions, and investigate the probability density function (PDF) for infection times in the system's steady state. Our findings show that recovery time distributions significantly affect disease dynamics, and we suggest several future research directions, including extending the model to arbitrary infection processes and using the quasistationary method to address deviations in numerical results.
References in corpus (20)
- The origin of bursts and heavy tails in human dynamics
- Dynamics of person-to-person interactions from distributed RFID sensor networks
- High resolution dynamical mapping of social interactions with active RFID
- Modeling bursts and heavy tails in human dynamics
- Uncovering individual and collective human dynamics from mobile phone records
- Information diffusion epidemics in social networks
- Human Dynamics: The Correspondence Patterns of Darwin and Einstein
- Impact of non-Poisson activity patterns on spreading processes
- Human dynamics revealed through Web analytics
- Spreading Dynamics Following Bursty Human Activity Patterns
- Human Activity in the Web
- Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes
- Spreading dynamics on networks: the role of burstiness, topology and non-stationarity
- Information Dynamics in Evolving Networks Based on the Birth-Death Process: Random Drift and Natural Selection Perspective
- Complex Network Modelling with Power-law Activating Patterns and Its Evolutionary Dynamics
- Small inter-event times govern epidemic spreading on temporal networks
- Non-Markovian epidemic spreading on temporal networks
- Stationarity of the inter-event power-law distributions
- Asymptotic absorption-time distributions in extinction-prone Markov processes
- Optimized reduction of uncertainty in bursty human dynamics