1,068 research outputs found
An efficient curing policy for epidemics on graphs
We provide a dynamic policy for the rapid containment of a contagion process
modeled as an SIS epidemic on a bounded degree undirected graph with n nodes.
We show that if the budget of curing resources available at each time is
, where is the CutWidth of the graph, and also of order
, then the expected time until the extinction of the epidemic
is of order , which is within a constant factor from optimal, as well
as sublinear in the number of nodes. Furthermore, if the CutWidth increases
only sublinearly with n, a sublinear expected time to extinction is possible
with a sublinearly increasing budget
Optimal curing policy for epidemic spreading over a community network with heterogeneous population
The design of an efficient curing policy, able to stem an epidemic process at
an affordable cost, has to account for the structure of the population contact
network supporting the contagious process. Thus, we tackle the problem of
allocating recovery resources among the population, at the lowest cost possible
to prevent the epidemic from persisting indefinitely in the network.
Specifically, we analyze a susceptible-infected-susceptible epidemic process
spreading over a weighted graph, by means of a first-order mean-field
approximation. First, we describe the influence of the contact network on the
dynamics of the epidemics among a heterogeneous population, that is possibly
divided into communities. For the case of a community network, our
investigation relies on the graph-theoretical notion of equitable partition; we
show that the epidemic threshold, a key measure of the network robustness
against epidemic spreading, can be determined using a lower-dimensional
dynamical system. Exploiting the computation of the epidemic threshold, we
determine a cost-optimal curing policy by solving a convex minimization
problem, which possesses a reduced dimension in the case of a community
network. Lastly, we consider a two-level optimal curing problem, for which an
algorithm is designed with a polynomial time complexity in the network size.Comment: to be published on Journal of Complex Network
When is a network epidemic hard to eliminate?
We consider the propagation of a contagion process (epidemic) on a network
and study the problem of dynamically allocating a fixed curing budget to the
nodes of the graph, at each time instant. For bounded degree graphs, we provide
a lower bound on the expected time to extinction under any such dynamic
allocation policy, in terms of a combinatorial quantity that we call the
resistance of the set of initially infected nodes, the available budget, and
the number of nodes n. Specifically, we consider the case of bounded degree
graphs, with the resistance growing linearly in n. We show that if the curing
budget is less than a certain multiple of the resistance, then the expected
time to extinction grows exponentially with n. As a corollary, if all nodes are
initially infected and the CutWidth of the graph grows linearly, while the
curing budget is less than a certain multiple of the CutWidth, then the
expected time to extinction grows exponentially in n. The combination of the
latter with our prior work establishes a fairly sharp phase transition on the
expected time to extinction (sub-linear versus exponential) based on the
relation between the CutWidth and the curing budget
A lower bound on the performance of dynamic curing policies for epidemics on graphs
We consider an SIS-type epidemic process that evolves on a known graph. We
assume that a fixed curing budget can be allocated at each instant to the nodes
of the graph, towards the objective of minimizing the expected extinction time
of the epidemic. We provide a lower bound on the optimal expected extinction
time as a function of the available budget, the epidemic parameters, the
maximum degree, and the CutWidth of the graph. For graphs with large CutWidth
(close to the largest possible), and under a budget which is sublinear in the
number of nodes, our lower bound scales exponentially with the size of the
graph
The Cost of Uncertainty in Curing Epidemics
Motivated by the study of controlling (curing) epidemics, we consider the
spread of an SI process on a known graph, where we have a limited budget to use
to transition infected nodes back to the susceptible state (i.e., to cure
nodes). Recent work has demonstrated that under perfect and instantaneous
information (which nodes are/are not infected), the budget required for curing
a graph precisely depends on a combinatorial property called the CutWidth. We
show that this assumption is in fact necessary: even a minor degradation of
perfect information, e.g., a diagnostic test that is 99% accurate, drastically
alters the landscape. Infections that could previously be cured in sublinear
time now may require exponential time, or orderwise larger budget to cure. The
crux of the issue comes down to a tension not present in the full information
case: if a node is suspected (but not certain) to be infected, do we risk
wasting our budget to try to cure an uninfected node, or increase our certainty
by longer observation, at the risk that the infection spreads further? Our
results present fundamental, algorithm-independent bounds that tradeoff budget
required vs. uncertainty.Comment: 35 pages, 3 figure
Halting viruses in scale-free networks
The vanishing epidemic threshold for viruses spreading on scale-free networks
indicate that traditional methods, aiming to decrease a virus' spreading rate
cannot succeed in eradicating an epidemic. We demonstrate that policies that
discriminate between the nodes, curing mostly the highly connected nodes, can
restore a finite epidemic threshold and potentially eradicate a virus. We find
that the more biased a policy is towards the hubs, the more chance it has to
bring the epidemic threshold above the virus' spreading rate. Furthermore, such
biased policies are more cost effective, requiring less cures to eradicate the
virus
Path-Based Epidemic Spreading in Networks
Conventional epidemic models assume omnidirectional contact-based infection. This strongly associates the epidemic spreading process with node degrees. The role of the infection transmission medium is often neglected. In real-world networks, however, the infectious agent as the physical contagion medium usually flows from one node to another via specific directed routes ( path-based infection). Here, we use continuous-time Markov chain analysis to model the influence of the infectious agent and routing paths on the spreading behavior by taking into account the state transitions of each node individually, rather than the mean aggregated behavior of all nodes. By applying a mean field approximation, the analysis complexity of the path-based infection mechanics is reduced from exponential to polynomial. We show that the structure of the topology plays a secondary role in determining the size of the epidemic. Instead, it is the routing algorithm and traffic intensity that determine the survivability and the steady-state of the epidemic. We define an infection characterization matrix that encodes both the routing and the traffic information. Based on this, we derive the critical path-based epidemic threshold below which the epidemic will die off, as well as conditional bounds of this threshold which network operators may use to promote/suppress path-based spreading in their networks. Finally, besides artificially generated random and scale-free graphs, we also use real-world networks and traffic, as case studies, in order to compare the behaviors of contact- and path-based epidemics. Our results further corroborate the recent empirical observations that epidemics in communication networks are highly persistent
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