Epidemic spreading and aging in temporal networks with memory
| Autor: | Michele Tizzani, Raffaella Burioni, Alessandro Vezzani, Enrico Ubaldi, Simone Lenti, Claudio Castellano |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Computer science Analytical predictions Epidemic dynamics FOS: Physical sciences Physics and Society (physics.soc-ph) Network topology 01 natural sciences 010305 fluids & plasmas Networks and Complex Systems 0103 physical sciences Quantitative Biology::Populations and Evolution Statistical physics Limit (mathematics) 010306 general physics Quantitative Biology - Populations and Evolution Epidemic control Social and Information Networks (cs.SI) Populations and Evolution (q-bio.PE) Computer Science - Social and Information Networks Function (mathematics) Computer Science::Social and Information Networks Articles Dynamic models Epidemic threshold Epidemic spreading FOS: Biological sciences Mean field approach |
| Zdroj: | Physical Review. E Physical review. E 98 (2018): 062315-1–062315-9. doi:10.1103/PhysRevE.98.062315 info:cnr-pdr/source/autori:Tizzani, Michele; Lenti, Simone; Ubaldi, Enrico; Vezzani, Alessandro; Castellano, Claudio; Burioni, Raffaella/titolo:Epidemic spreading and aging in temporal networks with memory/doi:10.1103%2FPhysRevE.98.062315/rivista:Physical review. E (Print)/anno:2018/pagina_da:062315-1/pagina_a:062315-9/intervallo_pagine:062315-1–062315-9/volume:98 |
| ISSN: | 2470-0053 2470-0045 |
| DOI: | 10.1103/PhysRevE.98.062315 |
| Popis: | Time-varying network topologies can deeply influence dynamical processes mediated by them. Memory effects in the pattern of interactions among individuals are also known to affect how diffusive and spreading phenomena take place. In this paper we analyze the combined effect of these two ingredients on epidemic dynamics on networks. We study the susceptible-infected-susceptible (SIS) and the susceptible-infected-removed (SIR) models on the recently introduced activity-driven networks with memory. By means of an activity-based mean-field approach we derive, in the long time limit, analytical predictions for the epidemic threshold as a function of the parameters describing the distribution of activities and the strength of the memory effects. Our results show that memory reduces the threshold, which is the same for SIS and SIR dynamics, therefore favouring epidemic spreading. The theoretical approach perfectly agrees with numerical simulations in the long time asymptotic regime. Strong aging effects are present in the preasymptotic regime and the epidemic threshold is deeply affected by the starting time of the epidemics. We discuss in detail the origin of the model-dependent preasymptotic corrections, whose understanding could potentially allow for epidemic control on correlated temporal networks. 10 pages, 8 fogures |
| Databáze: | OpenAIRE |
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