1,007 research outputs found
Network segregation in a model of misinformation and fact checking
Misinformation under the form of rumor, hoaxes, and conspiracy theories
spreads on social media at alarming rates. One hypothesis is that, since social
media are shaped by homophily, belief in misinformation may be more likely to
thrive on those social circles that are segregated from the rest of the
network. One possible antidote is fact checking which, in some cases, is known
to stop rumors from spreading further. However, fact checking may also backfire
and reinforce the belief in a hoax. Here we take into account the combination
of network segregation, finite memory and attention, and fact-checking efforts.
We consider a compartmental model of two interacting epidemic processes over a
network that is segregated between gullible and skeptic users. Extensive
simulation and mean-field analysis show that a more segregated network
facilitates the spread of a hoax only at low forgetting rates, but has no
effect when agents forget at faster rates. This finding may inform the
development of mitigation techniques and overall inform on the risks of
uncontrolled misinformation online
Validity of Markovian modeling for transient memory-dependent epidemic dynamics
The initial transient phase of an emerging epidemic is of critical importance
for data-driven model building, model-based prediction of the epidemic trend,
and articulation of control/prevention strategies. In principle, quantitative
models for real-world epidemics need to be memory-dependent or non-Markovian,
but this presents difficulties for data collection, parameter estimation,
computation and analyses. In contrast, the difficulties do not arise in the
traditional Markovian models. To uncover the conditions under which Markovian
and non-Markovian models are equivalent for transient epidemic dynamics is
outstanding and of significant current interest. We develop a comprehensive
computational and analytic framework to establish that the transient-state
equivalence holds when the average generation time matches and average removal
time, resulting in minimal Markovian estimation errors in the basic
reproduction number, epidemic forecasting, and evaluation of control strategy.
Strikingly, the errors depend on the generation-to-removal time ratio but not
on the specific values and distributions of these times, and this universality
will further facilitate prediction rectification. Overall, our study provides a
general criterion for modeling memory-dependent processes using the Markovian
frameworks
The influence of a transport process on the epidemic threshold
By generating transient encounters between individuals beyond their immediate
social environment, transport can have a profound impact on the spreading of an
epidemic. In this work, we consider epidemic dynamics in the presence of the
transport process that gives rise to a multiplex network model. In addition to
a static layer, the (multiplex) epidemic network consists of a second dynamic
layer in which any two individuals are connected for the time they occupy the
same site during a random walk they perform on a separate transport network. We
develop a mean-field description of the stochastic network model and study the
influence the transport process has on the epidemic threshold. We show that any
transport process generally lowers the epidemic threshold because of the
additional connections it generates. In contrast, considering also random walks
of fractional order that in some sense are a more realistic model of human
mobility, we find that these non-local transport dynamics raise the epidemic
threshold in comparison to a classical local random walk. We also test our
model on a realistic transport network (the Munich U-Bahn network), and
carefully compare mean-field solutions with stochastic trajectories in a range
of scenarios.Comment: Version as to appear in the Journal of Mathematical Biology with
revised figure
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
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