18,874 research outputs found
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
A framework for epidemic spreading in multiplex networks of metapopulations
We propose a theoretical framework for the study of epidemics in structured
metapopulations, with heterogeneous agents, subjected to recurrent mobility
patterns. We propose to represent the heterogeneity in the composition of the
metapopulations as layers in a multiplex network, where nodes would correspond
to geographical areas and layers account for the mobility patterns of agents of
the same class. We analyze both the classical Susceptible-Infected-Susceptible
and the Susceptible-Infected-Removed epidemic models within this framework, and
compare macroscopic and microscopic indicators of the spreading process with
extensive Monte Carlo simulations. Our results are in excellent agreement with
the simulations. We also derive an exact expression of the epidemic threshold
on this general framework revealing a non-trivial dependence on the mobility
parameter. Finally, we use this new formalism to address the spread of diseases
in real cities, specifically in the city of Medellin, Colombia, whose
population is divided into six socio-economic classes, each one identified with
a layer in this multiplex formalism.Comment: 13 pages, 11 figure
Activity driven modeling of time varying networks
Network modeling plays a critical role in identifying statistical
regularities and structural principles common to many systems. The large
majority of recent modeling approaches are connectivity driven. The structural
patterns of the network are at the basis of the mechanisms ruling the network
formation. Connectivity driven models necessarily provide a time-aggregated
representation that may fail to describe the instantaneous and fluctuating
dynamics of many networks. We address this challenge by defining the activity
potential, a time invariant function characterizing the agents' interactions
and constructing an activity driven model capable of encoding the instantaneous
time description of the network dynamics. The model provides an explanation of
structural features such as the presence of hubs, which simply originate from
the heterogeneous activity of agents. Within this framework, highly dynamical
networks can be described analytically, allowing a quantitative discussion of
the biases induced by the time-aggregated representations in the analysis of
dynamical processes.Comment: 10 pages, 4 figure
Temporal networks of face-to-face human interactions
The ever increasing adoption of mobile technologies and ubiquitous services
allows to sense human behavior at unprecedented levels of details and scale.
Wearable sensors are opening up a new window on human mobility and proximity at
the finest resolution of face-to-face proximity. As a consequence, empirical
data describing social and behavioral networks are acquiring a longitudinal
dimension that brings forth new challenges for analysis and modeling. Here we
review recent work on the representation and analysis of temporal networks of
face-to-face human proximity, based on large-scale datasets collected in the
context of the SocioPatterns collaboration. We show that the raw behavioral
data can be studied at various levels of coarse-graining, which turn out to be
complementary to one another, with each level exposing different features of
the underlying system. We briefly review a generative model of temporal contact
networks that reproduces some statistical observables. Then, we shift our focus
from surface statistical features to dynamical processes on empirical temporal
networks. We discuss how simple dynamical processes can be used as probes to
expose important features of the interaction patterns, such as burstiness and
causal constraints. We show that simulating dynamical processes on empirical
temporal networks can unveil differences between datasets that would otherwise
look statistically similar. Moreover, we argue that, due to the temporal
heterogeneity of human dynamics, in order to investigate the temporal
properties of spreading processes it may be necessary to abandon the notion of
wall-clock time in favour of an intrinsic notion of time for each individual
node, defined in terms of its activity level. We conclude highlighting several
open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series:
Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.
Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model
With the popularity of portable wireless devices it is important to model and
predict how information or contagions spread by natural human mobility -- for
understanding the spreading of deadly infectious diseases and for improving
delay tolerant communication schemes. Formally, we model this problem by
considering moving agents, where each agent initially carries a
\emph{distinct} bit of information. When two agents are at the same location or
in close proximity to one another, they share all their information with each
other. We would like to know the time it takes until all bits of information
reach all agents, called the \textit{flood time}, and how it depends on the way
agents move, the size and shape of the network and the number of agents moving
in the network.
We provide rigorous analysis for the \MRWP model (which takes paths with
minimum number of turns), a convenient model used previously to analyze mobile
agents, and find that with high probability the flood time is bounded by
, where agents move on an
grid. In addition to extensive simulations, we use a data set of
taxi trajectories to show that our method can successfully predict flood times
in both experimental settings and the real world.Comment: 10 pages, ACM SIGSPATIAL 2018, Seattle, U
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
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