4,492 research outputs found
Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers
Empirical data on contacts between individuals in social contexts play an
important role in providing information for models describing human behavior
and how epidemics spread in populations. Here, we analyze data on face-to-face
contacts collected in an office building. The statistical properties of
contacts are similar to other social situations, but important differences are
observed in the contact network structure. In particular, the contact network
is strongly shaped by the organization of the offices in departments, which has
consequences in the design of accurate agent-based models of epidemic spread.
We consider the contact network as a potential substrate for infectious disease
spread and show that its sparsity tends to prevent outbreaks of rapidly
spreading epidemics. Moreover, we define three typical behaviors according to
the fraction of links each individual shares outside its own department:
residents, wanderers and linkers. Linkers () act as bridges in the
network and have large betweenness centralities. Thus, a vaccination strategy
targeting linkers efficiently prevents large outbreaks. As such a behavior may
be spotted a priori in the offices' organization or from surveys, without the
full knowledge of the time-resolved contact network, this result may help the
design of efficient, low-cost vaccination or social-distancing 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
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
Mobile Human Ad Hoc Networks: A Communication Engineering Viewpoint on Interhuman Airborne Pathogen Transmission
Pathogens such as viruses and bacteria play a vital role in human life, since
they cause infectious diseases which can lead to epidemics. Recent coronavirus
disease 2019 epidemic has shown that taking effective prevention measures such
as wearing masks are important to reduce the human deaths and side effects of
the epidemic. It is therefore requisite to accurately model the spread of
infectious diseases whose one of the most crucial routes of transmission is
airborne transmission. The transmission models in the literature are proposed
independently from each other, at different scales and by the researchers from
various disciplines. Thus, there is a need to merge all these research
attempts. To this end, we propose a communication engineering approach that
melts different disciplines such as epidemiology, biology, medicine, and fluid
dynamics in the same pot to model airborne pathogen transmission among humans.
In this approach, we introduce the concept of mobile human ad hoc networks
(MoHANETs). This concept exploits the similarity of airborne
transmission-driven human groups with mobile ad hoc networks and uses molecular
communication as the enabling paradigm. The aim of this article is to present a
unified framework using communication engineering, and to highlight future
research directions for modeling the spread of infectious diseases among humans
through airborne pathogen transmission. In this article, we first review the
airborne pathogen transmission mechanisms. Then, the MoHANET is given with a
layered structure. In these layers, the infectious human emitting
pathogen-laden droplets through air and the exposed human to these droplets are
considered as the transmitter and receiver, respectively. Moreover, the
experimental methods for the proposed approach are reviewed and discussed.Comment: 7 pages, 6 figures, submitted to IEEE Communications Magazine Feature
Topic on Nano-Networking for Nano-, Micro-, and Macro-Scale Application
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
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