3,155 research outputs found
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
Networks and the epidemiology of infectious disease
The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues
Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history
Spatial spread of infectious diseases among populations via the mobility of
humans is highly stochastic and heterogeneous. Accurate forecast/mining of the
spread process is often hard to be achieved by using statistical or mechanical
models. Here we propose a new reverse problem, which aims to identify the
stochastically spatial spread process itself from observable information
regarding the arrival history of infectious cases in each subpopulation. We
solved the problem by developing an efficient optimization algorithm based on
dynamical programming, which comprises three procedures: i, anatomizing the
whole spread process among all subpopulations into disjoint componential
patches; ii, inferring the most probable invasion pathways underlying each
patch via maximum likelihood estimation; iii, recovering the whole process by
assembling the invasion pathways in each patch iteratively, without burdens in
parameter calibrations and computer simulations. Based on the entropy theory,
we introduced an identifiability measure to assess the difficulty level that an
invasion pathway can be identified. Results on both artificial and empirical
metapopulation networks show the robust performance in identifying actual
invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic
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