422 research outputs found
Prominence and Control: The Weighted Rich-Club Effect
Published in Physical Review Letters PRL 101, 168702 (2008)http://link.aps.org/doi/10.1103/PhysRevLett.101.168702. Copyright American Physical Society (APS).Publisher's note: Erratum in Phys Rev Lett. 2008 Oct 31;101(18):189903 http://link.aps.org/doi/10.1103/PhysRevLett.101.18990
Spread of Infectious Diseases with a Latent Period
Infectious diseases spread through human networks.
Susceptible-Infected-Removed (SIR) model is one of the epidemic models to
describe infection dynamics on a complex network connecting individuals. In the
metapopulation SIR model, each node represents a population (group) which has
many individuals. In this paper, we propose a modified metapopulation SIR model
in which a latent period is taken into account. We call it SIIR model. We
divide the infection period into two stages: an infected stage, which is the
same as the previous model, and a seriously ill stage, in which individuals are
infected and cannot move to the other populations. The two infectious stages in
our modified metapopulation SIR model produce a discontinuous final size
distribution. Individuals in the infected stage spread the disease like
individuals in the seriously ill stage and never recover directly, which makes
an effective recovery rate smaller than the given recovery rate.Comment: 6 pages, 3 figure
Random Networks with given Rich-club Coefficient
In complex networks it is common to model a network or generate a surrogate
network based on the conservation of the network's degree distribution. We
provide an alternative network model based on the conservation of connection
density within a set of nodes. This density is measure by the rich-club
coefficient. We present a method to generate surrogates networks with a given
rich-club coefficient. We show that by choosing a suitable local linking term,
the generated random networks can reproduce the degree distribution and the
mixing pattern of real networks. The method is easy to implement and produces
good models of real networks.Comment: revised version, new figure
WiFi Epidemiology: Can Your Neighbors' Router Make Yours Sick?
In densely populated urban areas WiFi routers form a tightly interconnected
proximity network that can be exploited as a substrate for the spreading of
malware able to launch massive fraudulent attack and affect entire urban areas
WiFi networks. In this paper we consider several scenarios for the deployment
of malware that spreads solely over the wireless channel of major urban areas
in the US. We develop an epidemiological model that takes into consideration
prevalent security flaws on these routers. The spread of such a contagion is
simulated on real-world data for geo-referenced wireless routers. We uncover a
major weakness of WiFi networks in that most of the simulated scenarios show
tens of thousands of routers infected in as little time as two weeks, with the
majority of the infections occurring in the first 24 to 48 hours. We indicate
possible containment and prevention measure to limit the eventual harm of such
an attack.Comment: 22 pages, 1 table, 4 figure
Internet data packet transport: from global topology to local queueing dynamics
We study structural feature and evolution of the Internet at the autonomous
systems level. Extracting relevant parameters for the growth dynamics of the
Internet topology, we construct a toy model for the Internet evolution, which
includes the ingredients of multiplicative stochastic evolution of nodes and
edges and adaptive rewiring of edges. The model reproduces successfully
structural features of the Internet at a fundamental level. We also introduce a
quantity called the load as the capacity of node needed for handling the
communication traffic and study its time-dependent behavior at the hubs across
years. The load at hub increases with network size as .
Finally, we study data packet traffic in the microscopic scale. The average
delay time of data packets in a queueing system is calculated, in particular,
when the number of arrival channels is scale-free. We show that when the number
of arriving data packets follows a power law distribution, ,
the queue length distribution decays as and the average delay
time at the hub diverges as in the limit when , being the network degree
exponent.Comment: 5 pages, 4 figures, submitted to International Journal of Bifurcation
and Chao
Phase transitions in contagion processes mediated by recurrent mobility patterns
Human mobility and activity patterns mediate contagion on many levels,
including the spatial spread of infectious diseases, diffusion of rumors, and
emergence of consensus. These patterns however are often dominated by specific
locations and recurrent flows and poorly modeled by the random diffusive
dynamics generally used to study them. Here we develop a theoretical framework
to analyze contagion within a network of locations where individuals recall
their geographic origins. We find a phase transition between a regime in which
the contagion affects a large fraction of the system and one in which only a
small fraction is affected. This transition cannot be uncovered by continuous
deterministic models due to the stochastic features of the contagion process
and defines an invasion threshold that depends on mobility parameters,
providing guidance for controlling contagion spread by constraining mobility
processes. We recover the threshold behavior by analyzing diffusion processes
mediated by real human commuting data.Comment: 20 pages of Main Text including 4 figures, 7 pages of Supplementary
Information; Nature Physics (2011
Epidemics in partially overlapped multiplex networks
Many real networks exhibit a layered structure in which links in each layer
reflect the function of nodes on different environments. These multiple types
of links are usually represented by a multiplex network in which each layer has
a different topology. In real-world networks, however, not all nodes are
present on every layer. To generate a more realistic scenario, we use a
generalized multiplex network and assume that only a fraction of the nodes
are shared by the layers. We develop a theoretical framework for a branching
process to describe the spread of an epidemic on these partially overlapped
multiplex networks. This allows us to obtain the fraction of infected
individuals as a function of the effective probability that the disease will be
transmitted . We also theoretically determine the dependence of the epidemic
threshold on the fraction of shared nodes in a system composed of two
layers. We find that in the limit of the threshold is dominated by
the layer with the smaller isolated threshold. Although a system of two
completely isolated networks is nearly indistinguishable from a system of two
networks that share just a few nodes, we find that the presence of these few
shared nodes causes the epidemic threshold of the isolated network with the
lower propagating capacity to change discontinuously and to acquire the
threshold of the other network.Comment: 13 pages, 4 figure
Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study
Background: The global spread of the severe acute respiratory syndrome (SARS)
epidemic has clearly shown the importance of considering the long-range
transportation networks in the understanding of emerging diseases outbreaks.
The introduction of extensive transportation data sets is therefore an
important step in order to develop epidemic models endowed with realism.
Methods: We develop a general stochastic meta-population model that
incorporates actual travel and census data among 3 100 urban areas in 220
countries. The model allows probabilistic predictions on the likelihood of
country outbreaks and their magnitude. The level of predictability offered by
the model can be quantitatively analyzed and related to the appearance of
robust epidemic pathways that represent the most probable routes for the spread
of the disease. Results: In order to assess the predictive power of the model,
the case study of the global spread of SARS is considered. The disease
parameter values and initial conditions used in the model are evaluated from
empirical data for Hong Kong. The outbreak likelihood for specific countries is
evaluated along with the emerging epidemic pathways. Simulation results are in
agreement with the empirical data of the SARS worldwide epidemic. Conclusions:
The presented computational approach shows that the integration of long-range
mobility and demographic data provides epidemic models with a predictive power
that can be consistently tested and theoretically motivated. This computational
strategy can be therefore considered as a general tool in the analysis and
forecast of the global spreading of emerging diseases and in the definition of
containment policies aimed at reducing the effects of potentially catastrophic
outbreaks.Comment: 21 pages, 2 tables, 7 figure
Multiscale mobility networks and the large scale spreading of infectious diseases
Among the realistic ingredients to be considered in the computational
modeling of infectious diseases, human mobility represents a crucial challenge
both on the theoretical side and in view of the limited availability of
empirical data. In order to study the interplay between small-scale commuting
flows and long-range airline traffic in shaping the spatio-temporal pattern of
a global epidemic we i) analyze mobility data from 29 countries around the
world and find a gravity model able to provide a global description of
commuting patterns up to 300 kms; ii) integrate in a worldwide structured
metapopulation epidemic model a time-scale separation technique for evaluating
the force of infection due to multiscale mobility processes in the disease
dynamics. Commuting flows are found, on average, to be one order of magnitude
larger than airline flows. However, their introduction into the worldwide model
shows that the large scale pattern of the simulated epidemic exhibits only
small variations with respect to the baseline case where only airline traffic
is considered. The presence of short range mobility increases however the
synchronization of subpopulations in close proximity and affects the epidemic
behavior at the periphery of the airline transportation infrastructure. The
present approach outlines the possibility for the definition of layered
computational approaches where different modeling assumptions and granularities
can be used consistently in a unifying multi-scale framework.Comment: 10 pages, 4 figures, 1 tabl
The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale
<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p
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