Epidemic spreading in modular time-varying networks
| Autor: | Matthieu Nadini, Kaiyuan Sun, Nicola Perra, Michele Starnini, Enrico Ubaldi, Alessandro Rizzo |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Física, Universitat Politècnica de Catalunya. CCQM - Condensed, Complex and Quantum Matter Group |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Physics - Physics and Society
Theoretical computer science Computer science Science FOS: Physical sciences Physics and Society (physics.soc-ph) 01 natural sciences Modularity Models Biological Article 010305 fluids & plasmas Reduction (complexity) 0103 physical sciences Epidèmies Humans Quantitative Biology::Populations and Evolution Computer Simulation 010306 general physics Epidemics Anàlisi numèrica Modularity (networks) Multidisciplinary Física [Àrees temàtiques de la UPC] business.industry Numerical Analysis Computer-Assisted Computer Science::Social and Information Networks Modular design Complex network H1 Medicine Disease Susceptibility business Numerical analysis |
| Zdroj: | Scientific Reports Scientific Reports, Vol 8, Iss 1, Pp 1-11 (2018) |
| Popis: | We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibits SIR processes, it speeds up SIS phenomena. In this case, we observe that modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal network. |
| Databáze: | OpenAIRE |
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