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Sampling methods for the quasistationary regime of epidemic processes on regular and complex networks

TLDR
This work compares different quasistationary simulation methods where the absorbing states are suitably handled and the thermodynamical limit of the original dynamics can be achieved and verifies the equivalence between RBC method and a weak external field for epidemics on networks.
Abstract
A major hurdle in the simulation of the steady state of epidemic processes is that the system will unavoidably visit an absorbing, disease-free state at sufficiently long times due to the finite size of the networks where epidemics evolves. In the present work, we compare different quasistationary (QS) simulation methods where the absorbing states are suitably handled and the thermodynamical limit of the original dynamics can be achieved. We analyze the standard QS (SQS) method, where the sampling is constrained to active configurations, the reflecting boundary condition (RBC), where the dynamics returns to the pre-absorbing configuration, and hub reactivation (HR), where the most connected vertex of the network is reactivated after a visit to an absorbing state. We apply the methods to the contact process (CP) and susceptible-infected-susceptible (SIS) models on regular and scale free networks. The investigated methods yield the same epidemic threshold for both models. For CP, that undergoes a standard collective phase transition, the methods are equivalent. For SIS, whose phase transition is ruled by the hub mutual reactivation, the SQS and HR methods are able to capture localized epidemic phases while RBC is not. We also apply the autocorrelation time as a tool to characterize the phase transition and observe that this analysis provides the same finite-size scaling exponents for the critical relaxation time for the investigated methods. Finally, we verify the equivalence between RBC method and a weak external field for epidemics on networks.

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Fundamentals of spreading processes in single and multilayer complex networks

TL;DR: The content presented here offers a whole suite of methods to study epidemic-like processes in complex networks, both for researchers without previous experience in the subject and for experts.
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Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks

TL;DR: Numerical simulation of continuous-time Markovian processes is an essential and widely applied tool in the investigation of epidemic spreading on complex networks and it is confirmed that the simulation outcomes of the optimized algorithms are statistically indistinguishable from the original GA and can be several orders of magnitude more efficient.
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Machine learning dynamical phase transitions in complex networks.

TL;DR: A general comprehensive machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology is developed.
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Robustness and fragility of the susceptible-infected-susceptible epidemic models on complex networks

TL;DR: Two alterations of the standard susceptible-infected-susceptible (SIS) dynamics that preserve the central properties of spontaneous healing and infection capacity of a vertex increasing unlimitedly with its degree are analyzed.
Journal ArticleDOI

Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

TL;DR: Using the susceptible-infected-susceptible dynamics, the approach reveals the existence of a mesoscopic localization regime, where a disease can concentrate and self-sustain only around large groups in the network overall organization.
References
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Book

Networks: An Introduction

Mark Newman
TL;DR: This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
Journal ArticleDOI

Epidemic Spreading in Scale-Free Networks

TL;DR: A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
Book

Numerical Recipes 3rd Edition: The Art of Scientific Computing

TL;DR: This new edition incorporates more than 400 Numerical Recipes routines, many of them new or upgraded, and adopts an object-oriented style particularly suited to scientific applications.
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