539,350 research outputs found
Epidemic space
The aim of this article is to highlight the importance of 'spatiality' in understanding the materialization of risk society and cultivation of risk sensibilities. More specifically it provides a cultural analysis of pathogen virulence (as a social phenomenon) by means of tracing and mapping the spatial flows that operate in the uncharted zones between the microphysics of infection and the macrophysics of epidemics. It will be argued that epidemic space consists of three types of forces: the vector, the index and the vortex. It will draw on Latour's Actor Network Theory to argue that epidemic space is geared towards instability when the vortex (of expanding associations and concerns) displaces the index (of finding a single cause)
Role of word-of-mouth for programs of voluntary vaccination: A game-theoretic approach
We propose a model describing the synergetic feedback between word-of-mouth
(WoM) and epidemic dynamics controlled by voluntary vaccination. We combine a
game-theoretic model for the spread of WoM and a compartmental model describing
disease dynamics in the presence of a program of voluntary vaccination.
We evaluate and compare two scenarios, depending on what WoM disseminates: (1)
vaccine advertising, which may occur whether or not an epidemic is ongoing and
(2) epidemic status, notably disease prevalence. Understanding the synergy
between the two strategies could be particularly important for organizing
voluntary vaccination campaigns. We find that, in the initial phase of an
epidemic, vaccination uptake is determined more by vaccine advertising than the
epidemic status. As the epidemic progresses, epidemic status become
increasingly important for vaccination uptake, considerably accelerating
vaccination uptake toward a stable vaccination coverage.Comment: 10 pages, 2 figure
Second look at the spread of epidemics on networks
In an important paper, M.E.J. Newman claimed that a general network-based
stochastic Susceptible-Infectious-Removed (SIR) epidemic model is isomorphic to
a bond percolation model, where the bonds are the edges of the contact network
and the bond occupation probability is equal to the marginal probability of
transmission from an infected node to a susceptible neighbor. In this paper, we
show that this isomorphism is incorrect and define a semi-directed random
network we call the epidemic percolation network that is exactly isomorphic to
the SIR epidemic model in any finite population. In the limit of a large
population, (i) the distribution of (self-limited) outbreak sizes is identical
to the size distribution of (small) out-components, (ii) the epidemic threshold
corresponds to the phase transition where a giant strongly-connected component
appears, (iii) the probability of a large epidemic is equal to the probability
that an initial infection occurs in the giant in-component, and (iv) the
relative final size of an epidemic is equal to the proportion of the network
contained in the giant out-component. For the SIR model considered by Newman,
we show that the epidemic percolation network predicts the same mean outbreak
size below the epidemic threshold, the same epidemic threshold, and the same
final size of an epidemic as the bond percolation model. However, the bond
percolation model fails to predict the correct outbreak size distribution and
probability of an epidemic when there is a nondegenerate infectious period
distribution. We confirm our findings by comparing predictions from percolation
networks and bond percolation models to the results of simulations. In an
appendix, we show that an isomorphism to an epidemic percolation network can be
defined for any time-homogeneous stochastic SIR model.Comment: 29 pages, 5 figure
Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing
In this paper, we outline the theory of epidemic percolation networks and
their use in the analysis of stochastic SIR epidemic models on undirected
contact networks. We then show how the same theory can be used to analyze
stochastic SIR models with random and proportionate mixing. The epidemic
percolation networks for these models are purely directed because undirected
edges disappear in the limit of a large population. In a series of simulations,
we show that epidemic percolation networks accurately predict the mean outbreak
size and probability and final size of an epidemic for a variety of epidemic
models in homogeneous and heterogeneous populations. Finally, we show that
epidemic percolation networks can be used to re-derive classical results from
several different areas of infectious disease epidemiology. In an appendix, we
show that an epidemic percolation network can be defined for any
time-homogeneous stochastic SIR model in a closed population and prove that the
distribution of outbreak sizes given the infection of any given node in the SIR
model is identical to the distribution of its out-component sizes in the
corresponding probability space of epidemic percolation networks. We conclude
that the theory of percolation on semi-directed networks provides a very
general framework for the analysis of stochastic SIR models in closed
populations.Comment: 40 pages, 9 figure
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