1,302 research outputs found
Analytical computation of the epidemic threshold on temporal networks
The time variation of contacts in a networked system may fundamentally alter
the properties of spreading processes and affect the condition for large-scale
propagation, as encoded in the epidemic threshold. Despite the great interest
in the problem for the physics, applied mathematics, computer science and
epidemiology communities, a full theoretical understanding is still missing and
currently limited to the cases where the time-scale separation holds between
spreading and network dynamics or to specific temporal network models. We
consider a Markov chain description of the Susceptible-Infectious-Susceptible
process on an arbitrary temporal network. By adopting a multilayer perspective,
we develop a general analytical derivation of the epidemic threshold in terms
of the spectral radius of a matrix that encodes both network structure and
disease dynamics. The accuracy of the approach is confirmed on a set of
temporal models and empirical networks and against numerical results. In
addition, we explore how the threshold changes when varying the overall time of
observation of the temporal network, so as to provide insights on the optimal
time window for data collection of empirical temporal networked systems. Our
framework is both of fundamental and practical interest, as it offers novel
understanding of the interplay between temporal networks and spreading
dynamics.Comment: 22 pages, 6 figure
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
Rich-club and page-club coefficients for directed graphs
Rich-club and page-club coefficients and their null models are introduced for
directed graphs. Null models allow for a quantitative discussion of the
rich-club and page-club phenomena. These coefficients are computed for four
directed real-world networks: Arxiv High Energy Physics paper citation network,
Web network (released from Google), Citation network among US Patents, and
Email network from a EU research institution. The results show a high
correlation between rich-club and page-club ordering. For journal paper
citation network, we identify both rich-club and page-club ordering, showing
that {}"elite" papers are cited by other {}"elite" papers. Google web network
shows partial rich-club and page-club ordering up to some point and then a
narrow declining of the corresponding normalized coefficients, indicating the
lack of rich-club ordering and the lack of page-club ordering, i.e. high
in-degree (PageRank) pages purposely avoid sharing links with other high
in-degree (PageRank) pages. For UC patents citation network, we identify
page-club and rich-club ordering providing a conclusion that {}"elite" patents
are cited by other {}"elite" patents. Finally, for e-mail communication network
we show lack of both rich-club and page-club ordering. We construct an example
of synthetic network showing page-club ordering and the lack of rich-club
ordering.Comment: 18 pages, 6 figure
Optimizing surveillance for livestock disease spreading through animal movements
The spatial propagation of many livestock infectious diseases critically
depends on the animal movements among premises; so the knowledge of movement
data may help us to detect, manage and control an outbreak. The identification
of robust spreading features of the system is however hampered by the temporal
dimension characterizing population interactions through movements. Traditional
centrality measures do not provide relevant information as results strongly
fluctuate in time and outbreak properties heavily depend on geotemporal initial
conditions. By focusing on the case study of cattle displacements in Italy, we
aim at characterizing livestock epidemics in terms of robust features useful
for planning and control, to deal with temporal fluctuations, sensitivity to
initial conditions and missing information during an outbreak. Through spatial
disease simulations, we detect spreading paths that are stable across different
initial conditions, allowing the clustering of the seeds and reducing the
epidemic variability. Paths also allow us to identify premises, called
sentinels, having a large probability of being infected and providing critical
information on the outbreak origin, as encoded in the clusters. This novel
procedure provides a general framework that can be applied to specific
diseases, for aiding risk assessment analysis and informing the design of
optimal surveillance systems.Comment: Supplementary Information at
https://sites.google.com/site/paolobajardi/Home/archive/optimizing_surveillance_ESM_l.pdf?attredirects=
Epidemic Threshold in Continuous-Time Evolving Networks
Current understanding of the critical outbreak condition on temporal networks
relies on approximations (time scale separation, discretization) that may bias
the results. We propose a theoretical framework to compute the epidemic
threshold in continuous time through the infection propagator approach. We
introduce the {\em weak commutation} condition allowing the interpretation of
annealed networks, activity-driven networks, and time scale separation into one
formalism. Our work provides a coherent connection between discrete and
continuous time representations applicable to realistic scenarios.Comment: 13 pages, 2 figure
Prediction and predictability of global epidemics: the role of the airline transportation network
The systematic study of large-scale networks has unveiled the ubiquitous
presence of connectivity patterns characterized by large scale heterogeneities
and unbounded statistical fluctuations. These features affect dramatically the
behavior of the diffusion processes occurring on networks, determining the
ensuing statistical properties of their evolution pattern and dynamics. In this
paper, we investigate the role of the large scale properties of the airline
transportation network in determining the global evolution of emerging disease.
We present a stochastic computational framework for the forecast of global
epidemics that considers the complete world-wide air travel infrastructure
complemented with census population data. We address two basic issues in global
epidemic modeling: i) We study the role of the large scale properties of the
airline transportation network in determining the global diffusion pattern of
emerging diseases; ii) We evaluate the reliability of forecasts and outbreak
scenarios with respect to the intrinsic stochasticity of disease transmission
and traffic flows. In order to address these issues we define a set of novel
quantitative measures able to characterize the level of heterogeneity and
predictability of the epidemic pattern. These measures may be used for the
analysis of containment policies and epidemic risk assessment.Comment: 20 pages, 5 figure
Predicting epidemic risk from past temporal contact data
Understanding how epidemics spread in a system is a crucial step to prevent
and control outbreaks, with broad implications on the system's functioning,
health, and associated costs. This can be achieved by identifying the elements
at higher risk of infection and implementing targeted surveillance and control
measures. One important ingredient to consider is the pattern of
disease-transmission contacts among the elements, however lack of data or
delays in providing updated records may hinder its use, especially for
time-varying patterns. Here we explore to what extent it is possible to use
past temporal data of a system's pattern of contacts to predict the risk of
infection of its elements during an emerging outbreak, in absence of updated
data. We focus on two real-world temporal systems; a livestock displacements
trade network among animal holdings, and a network of sexual encounters in
high-end prostitution. We define the node's loyalty as a local measure of its
tendency to maintain contacts with the same elements over time, and uncover
important non-trivial correlations with the node's epidemic risk. We show that
a risk assessment analysis incorporating this knowledge and based on past
structural and temporal pattern properties provides accurate predictions for
both systems. Its generalizability is tested by introducing a theoretical model
for generating synthetic temporal networks. High accuracy of our predictions is
recovered across different settings, while the amount of possible predictions
is system-specific. The proposed method can provide crucial information for the
setup of targeted intervention strategies.Comment: 24 pages, 5 figures + SI (18 pages, 15 figures
Impact of spatially constrained sampling of temporal contact networks on the evaluation of the epidemic risk
The ability to directly record human face-to-face interactions increasingly
enables the development of detailed data-driven models for the spread of
directly transmitted infectious diseases at the scale of individuals. Complete
coverage of the contacts occurring in a population is however generally
unattainable, due for instance to limited participation rates or experimental
constraints in spatial coverage. Here, we study the impact of spatially
constrained sampling on our ability to estimate the epidemic risk in a
population using such detailed data-driven models. The epidemic risk is
quantified by the epidemic threshold of the
susceptible-infectious-recovered-susceptible model for the propagation of
communicable diseases, i.e. the critical value of disease transmissibility
above which the disease turns endemic. We verify for both synthetic and
empirical data of human interactions that the use of incomplete data sets due
to spatial sampling leads to the underestimation of the epidemic risk. The bias
is however smaller than the one obtained by uniformly sampling the same
fraction of contacts: it depends nonlinearly on the fraction of contacts that
are recorded and becomes negligible if this fraction is large enough. Moreover,
it depends on the interplay between the timescales of population and spreading
dynamics.Comment: 21 pages, 7 figure
Characterising two-pathogen competition in spatially structured environments
Different pathogens spreading in the same host population often generate
complex co-circulation dynamics because of the many possible interactions
between the pathogens and the host immune system, the host life cycle, and the
space structure of the population. Here we focus on the competition between two
acute infections and we address the role of host mobility and cross-immunity in
shaping possible dominance/co-dominance regimes. Host mobility is modelled as a
network of traveling flows connecting nodes of a metapopulation, and the
two-pathogen dynamics is simulated with a stochastic mechanistic approach.
Results depict a complex scenario where, according to the relation among the
epidemiological parameters of the two pathogens, mobility can either be
non-influential for the competition dynamics or play a critical role in
selecting the dominant pathogen. The characterisation of the parameter space
can be explained in terms of the trade-off between pathogen's spreading
velocity and its ability to diffuse in a sparse environment. Variations in the
cross-immunity level induce a transition between presence and absence of
competition. The present study disentangles the role of the relevant biological
and ecological factors in the competition dynamics, and provides relevant
insights into the spatial ecology of infectious diseases.Comment: 30 pages, 6 figures, 1 table. Final version accepted for publication
in Scientific Report
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