3,364 research outputs found
Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks
We consider the problem of controlling the propagation of an epidemic
outbreak in an arbitrary contact network by distributing vaccination resources
throughout the network. We analyze a networked version of the
Susceptible-Infected-Susceptible (SIS) epidemic model when individuals in the
network present different levels of susceptibility to the epidemic. In this
context, controlling the spread of an epidemic outbreak can be written as a
spectral condition involving the eigenvalues of a matrix that depends on the
network structure and the parameters of the model. We study the problem of
finding the optimal distribution of vaccines throughout the network to control
the spread of an epidemic outbreak. We propose a convex framework to find
cost-optimal distribution of vaccination resources when different levels of
vaccination are allowed. We also propose a greedy approach with quality
guarantees for the case of all-or-nothing vaccination. We illustrate our
approaches with numerical simulations in a real social network
Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models
We propose a novel framework to study viral spreading processes in
metapopulation models. Large subpopulations (i.e., cities) are connected via
metalinks (i.e., roads) according to a metagraph structure (i.e., the traffic
infrastructure). The problem of containing the propagation of an epidemic
outbreak in a metapopulation model by controlling the traffic between
subpopulations is considered. Controlling the spread of an epidemic outbreak
can be written as a spectral condition involving the eigenvalues of a matrix
that depends on the network structure and the parameters of the model. Based on
this spectral condition, we propose a convex optimization framework to find
cost-optimal approaches to traffic control in epidemic outbreaks
Die-out Probability in SIS Epidemic Processes on Networks
An accurate approximate formula of the die-out probability in a SIS epidemic
process on a network is proposed. The formula contains only three essential
parameters: the largest eigenvalue of the adjacency matrix of the network, the
effective infection rate of the virus, and the initial number of infected nodes
in the network. The die-out probability formula is compared with the exact
die-out probability in complete graphs, Erd\H{o}s-R\'enyi graphs, and a
power-law graph. Furthermore, as an example, the formula is applied to the
-Intertwined Mean-Field Approximation, to explicitly incorporate the
die-out.Comment: Version2: 10 figures, 11 pagers. Corrected typos; simulation results
of ER graphs and a power-law graph are added. Accepted by the 5th
International Workshop on Complex Networks and their Applications, November
30 - December 02, 2016, Milan, Ital
Containing epidemic outbreaks by message-passing techniques
The problem of targeted network immunization can be defined as the one of
finding a subset of nodes in a network to immunize or vaccinate in order to
minimize a tradeoff between the cost of vaccination and the final (stationary)
expected infection under a given epidemic model. Although computing the
expected infection is a hard computational problem, simple and efficient
mean-field approximations have been put forward in the literature in recent
years. The optimization problem can be recast into a constrained one in which
the constraints enforce local mean-field equations describing the average
stationary state of the epidemic process. For a wide class of epidemic models,
including the susceptible-infected-removed and the
susceptible-infected-susceptible models, we define a message-passing approach
to network immunization that allows us to study the statistical properties of
epidemic outbreaks in the presence of immunized nodes as well as to find
(nearly) optimal immunization sets for a given choice of parameters and costs.
The algorithm scales linearly with the size of the graph and it can be made
efficient even on large networks. We compare its performance with topologically
based heuristics, greedy methods, and simulated annealing
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