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Contrasting effects of strong ties on SIR and SIS processes in temporal networks

Contrasting effects of strong ties on SIR and SIS processes in temporal networks

Sun, Kaiyuan, Baronchelli, Andrea and Perra, Nicola ORCID: 0000-0002-5559-3064 (2015) Contrasting effects of strong ties on SIR and SIS processes in temporal networks. The European Physical Journal B - Condensed Matter, 88 (12):326. pp. 1-8. ISSN 1434-6028 (Print), 1434-6036 (Online) (doi:

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Most real networks are characterized by connectivity patterns that evolve in time following complex, non-Markovian, dynamics. Here we investigate the impact of this ubiquitous feature by studying the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) epidemic models on activity driven networks with and without memory (i.e., Markovian and non-Markovian). We find that memory inhibits the spreading process in SIR models by shifting the epidemic threshold to larger values and reducing the final fraction of recovered nodes. In SIS processes instead, memory reduces the epidemic threshold and, for a wide range of diseases' parameters, increases the fraction of nodes affected by the disease in the endemic state. The heterogeneity in tie strengths, and the frequent repetition of strong ties it entails, allows in fact less virulent SIS-like diseases to survive in tightly connected local clusters that serve as reservoir for the virus. We validate this picture by studying both processes on two real temporal networks.

Item Type: Article
Additional Information: Approved for Open Access through Springer Compact arrangement. OA license: CC-BY-4.0
Uncontrolled Keywords: Temporal networks, Dynamical processes on time-varying networks
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Faculty / Department / Research Group: Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified: 21 Oct 2020 10:05
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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