21 research outputs found
What's in a crowd? Analysis of face-to-face behavioral networks
The availability of new data sources on human mobility is opening new avenues
for investigating the interplay of social networks, human mobility and
dynamical processes such as epidemic spreading. Here we analyze data on the
time-resolved face-to-face proximity of individuals in large-scale real-world
scenarios. We compare two settings with very different properties, a scientific
conference and a long-running museum exhibition. We track the behavioral
networks of face-to-face proximity, and characterize them from both a static
and a dynamic point of view, exposing important differences as well as striking
similarities. We use our data to investigate the dynamics of a
susceptible-infected model for epidemic spreading that unfolds on the dynamical
networks of human proximity. The spreading patterns are markedly different for
the conference and the museum case, and they are strongly impacted by the
causal structure of the network data. A deeper study of the spreading paths
shows that the mere knowledge of static aggregated networks would lead to
erroneous conclusions about the transmission paths on the dynamical networks
Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors
Contacts between patients, patients and health care workers (HCWs) and among
HCWs represent one of the important routes of transmission of hospital-acquired
infections (HAI). A detailed description and quantification of contacts in
hospitals provides key information for HAIs epidemiology and for the design and
validation of control measures. We used wearable sensors to detect close-range
interactions ("contacts") between individuals in the geriatric unit of a
university hospital. Contact events were measured with a spatial resolution of
about 1.5 meters and a temporal resolution of 20 seconds. The study included 46
HCWs and 29 patients and lasted for 4 days and 4 nights. 14037 contacts were
recorded. The number and duration of contacts varied between mornings,
afternoons and nights, and contact matrices describing the mixing patterns
between HCW and patients were built for each time period. Contact patterns were
qualitatively similar from one day to the next. 38% of the contacts occurred
between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts
including at least one patient, suggesting a population of individuals who
could potentially act as super-spreaders. Wearable sensors represent a novel
tool for the measurement of contact patterns in hospitals. The collected data
provides information on important aspects that impact the spreading patterns of
infectious diseases, such as the strong heterogeneity of contact numbers and
durations across individuals, the variability in the number of contacts during
a day, and the fraction of repeated contacts across days. This variability is
associated with a marked statistical stability of contact and mixing patterns
across days. Our results highlight the need for such measurement efforts in
order to correctly inform mathematical models of HAIs and use them to inform
the design and evaluation of prevention strategies
Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees
The spread of infectious diseases crucially depends on the pattern of
contacts among individuals. Knowledge of these patterns is thus essential to
inform models and computational efforts. Few empirical studies are however
available that provide estimates of the number and duration of contacts among
social groups. Moreover, their space and time resolution are limited, so that
data is not explicit at the person-to-person level, and the dynamical aspect of
the contacts is disregarded. Here, we want to assess the role of data-driven
dynamic contact patterns among individuals, and in particular of their temporal
aspects, in shaping the spread of a simulated epidemic in the population.
We consider high resolution data of face-to-face interactions between the
attendees of a conference, obtained from the deployment of an infrastructure
based on Radio Frequency Identification (RFID) devices that assess mutual
face-to-face proximity. The spread of epidemics along these interactions is
simulated through an SEIR model, using both the dynamical network of contacts
defined by the collected data, and two aggregated versions of such network, in
order to assess the role of the data temporal aspects.
We show that, on the timescales considered, an aggregated network taking into
account the daily duration of contacts is a good approximation to the full
resolution network, whereas a homogeneous representation which retains only the
topology of the contact network fails in reproducing the size of the epidemic.
These results have important implications in understanding the level of
detail needed to correctly inform computational models for the study and
management of real epidemics
Identifying Close Friendships in a Sensed Social Network
Studies have suggested that propinquity; social, cultural, physical and psychological similarities are major factors in close friendship ties. These studies were subject to human recall of interactions with no details of length or time of interactions. Recently, advancements in mobile technology have enabled the measurement of complex systems of interactions. This study uses social network analysis of data comprising of time-resolved sensed interactions to predict and explain close friendship ties via interactions at different periods, residence (floor) similarity and gender similarity. Results indicate residence (floor) proximity and duration of weekend night interactions have the potential of explaining close friendship ties.MIT Masdar Progra
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
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
High-resolution measurements of face-to-face contact patterns in a primary school
Little quantitative information is available on the mixing patterns of
children in school environments. Describing and understanding contacts between
children at school would help quantify the transmission opportunities of
respiratory infections and identify situations within schools where the risk of
transmission is higher. We report on measurements carried out in a French
school (6-12 years children), where we collected data on the time-resolved
face-to-face proximity of children and teachers using a proximity-sensing
infrastructure based on radio frequency identification devices.
Data on face-to-face interactions were collected on October 1st and 2nd,
2009. We recorded 77,602 contact events between 242 individuals. Each child has
on average 323 contacts per day with 47 other children, leading to an average
daily interaction time of 176 minutes. Most contacts are brief, but long
contacts are also observed. Contacts occur mostly within each class, and each
child spends on average three times more time in contact with classmates than
with children of other classes. We describe the temporal evolution of the
contact network and the trajectories followed by the children in the school,
which constrain the contact patterns. We determine an exposure matrix aimed at
informing mathematical models. This matrix exhibits a class and age structure
which is very different from the homogeneous mixing hypothesis.
The observed properties of the contact patterns between school children are
relevant for modeling the propagation of diseases and for evaluating control
measures. We discuss public health implications related to the management of
schools in case of epidemics and pandemics. Our results can help define a
prioritization of control measures based on preventive measures, case
isolation, classes and school closures, that could reduce the disruption to
education during epidemics
Nothing is in the air
It has often been argued that “there is something in the air” which makes firms in high-density environments—such as cities or clusters—more innovative. The co-location of firms facilitates the emergence of serendipity and casual encounters which promote innovation in firms. We assess this hypothesis using data from a survey of Norwegian firms engaged in innovation partnerships. The results indicate that there may be “much less in the air” than is generally assumed in the literature. The relationships conducive to innovation by Norwegian firms emerged as a consequence of purpose-built searches and had little to do with chance, serendipity, or “being there.”acceptedVersio
An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices
The integration of empirical data in computational frameworks to model the
spread of infectious diseases poses challenges that are becoming pressing with
the increasing availability of high-resolution information on human mobility
and contacts. This deluge of data has the potential to revolutionize the
computational efforts aimed at simulating scenarios and designing containment
strategies. However, the integration of detailed data sources yields models
that are less transparent and general. Hence, given a specific disease model,
it is crucial to assess which representations of the raw data strike the best
balance between simplicity and detail. We consider high-resolution data on the
face-to-face interactions of individuals in a hospital ward, obtained by using
wearable proximity sensors. We simulate the spread of a disease in this
community by using an SEIR model on top of different mathematical
representations of the contact patterns. We show that a contact matrix that
only contains average contact durations fails to reproduce the size of the
epidemic obtained with the high-resolution contact data and also to identify
the most at-risk classes. We introduce a contact matrix of probability
distributions that takes into account the heterogeneity of contact durations
between (and within) classes of individuals, and we show that this
representation yields a good approximation of the epidemic spreading properties
obtained by using the high-resolution data. Our results mark a step towards the
definition of synopses of high-resolution dynamic contact networks, providing a
compact representation of contact patterns that can correctly inform
computational models designed to discover risk groups and evaluate containment
policies. We show that this novel kind of representation can preserve in
simulation quantitative features of the epidemics that are crucial for their
study and management