349 research outputs found
Contact patterns among high school students
Face-to-face contacts between individuals contribute to shape social networks
and play an important role in determining how infectious diseases can spread
within a population. It is thus important to obtain accurate and reliable
descriptions of human contact patterns occurring in various day-to-day life
contexts. Recent technological advances and the development of wearable sensors
able to sense proximity patterns have made it possible to gather data giving
access to time-varying contact networks of individuals in specific
environments. Here we present and analyze two such data sets describing with
high temporal resolution the contact patterns of students in a high school. We
define contact matrices describing the contact patterns between students of
different classes and show the importance of the class structure. We take
advantage of the fact that the two data sets were collected in the same setting
during several days in two successive years to perform a longitudinal analysis
on two very different timescales. We show the high stability of the contact
patterns across days and across years: the statistical distributions of numbers
and durations of contacts are the same in different periods, and we observe a
very high similarity of the contact matrices measured in different days or
different years. The rate of change of the contacts of each individual from one
day to the next is also similar in different years. We discuss the interest of
the present analysis and data sets for various fields, including in social
sciences in order to better understand and model human behavior and
interactions in different contexts, and in epidemiology in order to inform
models describing the spread of infectious diseases and design targeted
containment strategies.Comment: Supplementary Information at
http://s3-eu-west-1.amazonaws.com/files.figshare.com/1677807/File_S1.pd
Estimating the epidemic risk using non-uniformly sampled contact data
Many datasets describing contacts in a population suffer from incompleteness
due to population sampling and underreporting of contacts. Data-driven
simulations of spreading processes using such incomplete data lead to an
underestimation of the epidemic risk, and it is therefore important to devise
methods to correct this bias. We focus here on a non-uniform sampling of the
contacts between individuals, aimed at mimicking the results of diaries or
surveys, and consider as case studies two datasets collected in different
contexts. We show that using surrogate data built using a method developed in
the case of uniform population sampling yields an improvement with respect to
the use of the sampled data but is strongly limited by the underestimation of
the link density in the sampled network. We put forward a second method to
build surrogate data that assumes knowledge of the density of links within one
of the groups forming the population. We show that it gives very good results
when the population is strongly structured, and discuss its limitations in the
case of a population with a weaker group structure. These limitations highlight
the interest of measurements using wearable sensors able to yield accurate
information on the structure and durations of contacts
Random inelasticity and velocity fluctuations in a driven granular gas
We analyze the deviations from Maxwell-Boltzmann statistics found in recent
experiments studying velocity distributions in two-dimensional granular gases
driven into a non-equilibrium stationary state by a strong vertical vibration.
We show that in its simplest version, the ``stochastic thermostat'' model of
heated inelastic hard spheres, contrary to what has been hitherto stated, is
incompatible with the experimental data, although predicting a reminiscent high
velocity stretched exponential behavior with an exponent 3/2. The experimental
observations lead to refine a recently proposed random restitution coefficient
model. Very good agreement is then found with experimental velocity
distributions within this framework, which appears self-consistent and further
provides relevant probes to investigate the universality of the velocity
statistics.Comment: 5 pages, 5 eps figure
Can co-location be used as a proxy for face-to-face contacts?
Technological advances have led to a strong increase in the number of data
collection efforts aimed at measuring co-presence of individuals at different
spatial resolutions. It is however unclear how much co-presence data can inform
us on actual face-to-face contacts, of particular interest to study the
structure of a population in social groups or for use in data-driven models of
information or epidemic spreading processes. Here, we address this issue by
leveraging data sets containing high resolution face-to-face contacts as well
as a coarser spatial localisation of individuals, both temporally resolved, in
various contexts. The co-presence and the face-to-face contact temporal
networks share a number of structural and statistical features, but the former
is (by definition) much denser than the latter. We thus consider several
down-sampling methods that generate surrogate contact networks from the
co-presence signal and compare them with the real face-to-face data. We show
that these surrogate networks reproduce some features of the real data but are
only partially able to identify the most central nodes of the face-to-face
network. We then address the issue of using such down-sampled co-presence data
in data-driven simulations of epidemic processes, and in identifying efficient
containment strategies. We show that the performance of the various sampling
methods strongly varies depending on context. We discuss the consequences of
our results with respect to data collection strategies and methodologies
Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks
Contacts between individuals play an important role in determining how
infectious diseases spread. Various methods to gather data on such contacts
co-exist, from surveys to wearable sensors. Comparisons of data obtained by
different methods in the same context are however scarce, in particular with
respect to their use in data-driven models of spreading processes. Here, we use
a combined data set describing contacts registered by sensors and friendship
relations in the same population to address this issue in a case study. We
investigate if the use of the friendship network is equivalent to a sampling
procedure performed on the sensor contact network with respect to the outcome
of simulations of spreading processes: such an equivalence might indeed give
hints on ways to compensate for the incompleteness of contact data deduced from
surveys. We show that this is indeed the case for these data, for a
specifically designed sampling procedure, in which respondents report their
neighbors with a probability depending on their contact time. We study the
impact of this specific sampling procedure on several data sets, discuss
limitations of our approach and its possible applications in the use of data
sets of various origins in data-driven simulations of epidemic processes
Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys
Given their importance in shaping social networks and determining how
information or diseases propagate in a population, human interactions are the
subject of many data collection efforts. To this aim, different methods are
commonly used, from diaries and surveys to wearable sensors. These methods show
advantages and limitations but are rarely compared in a given setting. As
surveys targeting friendship relations might suffer less from memory biases
than contact diaries, it is also interesting to explore how daily contact
patterns compare with friendship relations and with online social links. Here
we make progresses in these directions by leveraging data from a French high
school: face-to-face contacts measured by two concurrent methods, sensors and
diaries; self-reported friendship surveys; Facebook links. We compare the data
sets and find that most short contacts are not reported in diaries while long
contacts have larger reporting probability, with a general tendency to
overestimate durations. Measured contacts corresponding to reported friendship
can have durations of any length but all long contacts correspond to reported
friendships. Online links not associated to reported friendships correspond to
short face-to-face contacts, highlighting the different nature of reported
friendships and online links. Diaries and surveys suffer from a low sampling
rate, showing the higher acceptability of sensor-based platform. Despite the
biases, we found that the overall structure of the contact network, i.e., the
mixing patterns between classes, is correctly captured by both self-reported
contacts and friendships networks. Overall, diaries and surveys tend to yield a
correct picture of the structural organization of the contact network, albeit
with much less links, and give access to a sort of backbone of the contact
network corresponding to the strongest links in terms of cumulative durations
Mitigation of infectious disease at school: targeted class closure vs school closure
School environments are thought to play an important role in the community
spread of airborne infections (e.g., influenza) because of the high mixing
rates of school children. The closure of schools has therefore been proposed as
efficient mitigation strategy, with however high social and economic costs:
alternative, less disruptive interventions are highly desirable. The recent
availability of high-resolution contact networks in school environments
provides an opportunity to design micro-interventions and compare the outcomes
of alternative mitigation measures. We consider mitigation measures that
involve the targeted closure of school classes or grades based on readily
available information such as the number of symptomatic infectious children in
a class. We focus on the case of a primary school for which we have
high-resolution data on the close-range interactions of children and teachers.
We simulate the spread of an influenza-like illness in this population by using
an SEIR model with asymptomatics and compare the outcomes of different
mitigation strategies. We find that targeted class closure affords strong
mitigation effects: closing a class for a fixed period of time -equal to the
sum of the average infectious and latent durations- whenever two infectious
individuals are detected in that class decreases the attack rate by almost 70%
and strongly decreases the probability of a severe outbreak. The closure of all
classes of the same grade mitigates the spread almost as much as closing the
whole school. Targeted class closure strategies based on readily available
information on symptomatic subjects and on limited information on mixing
patterns, such as the grade structure of the school, can be almost as effective
as whole-school closure, at a much lower cost. This may inform public health
policies for the management and mitigation of influenza-like outbreaks in the
community
Glass transition and random walks on complex energy landscapes
We present a simple mathematical model of glassy dynamics seen as a random
walk in a directed, weighted network of minima taken as a representation of the
energy landscape. Our approach gives a broader perspective to previous studies
focusing on particular examples of energy landscapes obtained by sampling
energy minima and saddles of small systems. We point out how the relation
between the energies of the minima and their number of neighbors should be
studied in connection with the network's global topology, and show how the
tools developed in complex network theory can be put to use in this context
Effect of risk perception on epidemic spreading in temporal networks
Many progresses in the understanding of epidemic spreading models have been
obtained thanks to numerous modeling efforts and analytical and numerical
studies, considering host populations with very different structures and
properties, including complex and temporal interaction networks. Moreover, a
number of recent studies have started to go beyond the assumption of an absence
of coupling between the spread of a disease and the structure of the contacts
on which it unfolds. Models including awareness of the spread have been
proposed, to mimic possible precautionary measures taken by individuals that
decrease their risk of infection, but have mostly considered static networks.
Here, we adapt such a framework to the more realistic case of temporal networks
of interactions between individuals. We study the resulting model by analytical
and numerical means on both simple models of temporal networks and empirical
time-resolved contact data. Analytical results show that the epidemic threshold
is not affected by the awareness but that the prevalence can be significantly
decreased. Numerical studies highlight however the presence of very strong
finite-size effects, in particular for the more realistic synthetic temporal
networks, resulting in a significant shift of the effective epidemic threshold
in the presence of risk awareness. For empirical contact networks, the
awareness mechanism leads as well to a shift in the effective threshold and to
a strong reduction of the epidemic prevalence
How memory generates heterogeneous dynamics in temporal networks
Empirical temporal networks display strong heterogeneities in their dynamics,
which profoundly affect processes taking place on these networks, such as rumor
and epidemic spreading. Despite the recent wealth of data on temporal networks,
little work has been devoted to the understanding of how such heterogeneities
can emerge from microscopic mechanisms at the level of nodes and links. Here we
show that long-term memory effects are present in the creation and
disappearance of links in empirical networks. We thus consider a simple
generative modeling framework for temporal networks able to incorporate these
memory mechanisms. This allows us to study separately the role of each of these
mechanisms in the emergence of heterogeneous network dynamics. In particular,
we show analytically and numerically how heterogeneous distributions of contact
durations, of inter-contact durations and of numbers of contacts per link
emerge. We also study the individual effect of heterogeneities on dynamical
processes, such as the paradigmatic Susceptible-Infected epidemic spreading
model. Our results confirm in particular the crucial role of the distributions
of inter-contact durations and of the numbers of contacts per link
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