9,354 research outputs found
Stochastic Optimal Control of Epidemic Processes in Networks
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps. In contrast to previous work, this novel perspective is particularly well-suited to make use of fine-grained data about disease outbreaks and lets us overcome the shortcomings of current control strategies. Our control strategy resorts to treatment intensities to determine who to treat and when to do so to minimize the amount of infected individuals over time. Preliminary experiments with synthetic data show that our control strategy consistently outperforms several alternatives. Looking into the future, we believe our methodology provides a promising step towards the development of practical data-driven control strategies of epidemic processes
Control of Time-Varying Epidemic-Like Stochastic Processes and Their Mean-Field Limits
The optimal control of epidemic-like stochastic processes is important both
historically and for emerging applications today, where it can be especially
important to include time-varying parameters that impact viral epidemic-like
propagation. We connect the control of such stochastic processes with
time-varying behavior to the stochastic shortest path problem and obtain
solutions for various cost functions. Then, under a mean-field scaling, this
general class of stochastic processes is shown to converge to a corresponding
dynamical system. We analogously establish that the optimal control of this
class of processes converges to the optimal control of the limiting dynamical
system. Consequently, we study the optimal control of the dynamical system
where the comparison of both controlled systems renders various important
mathematical properties of interest.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0798
Spreading Processes over Socio-Technical Networks with Phase-Type Transmissions
Most theoretical tools available for the analysis of spreading processes over
networks assume exponentially distributed transmission and recovery times. In
practice, the empirical distribution of transmission times for many real
spreading processes, such as the spread of web content through the Internet,
are far from exponential. To bridge this gap between theory and practice, we
propose a methodology to model and analyze spreading processes with arbitrary
transmission times using phase-type distributions. Phase-type distributions are
a family of distributions that is dense in the set of positive-valued
distributions and can be used to approximate any given distributions. To
illustrate our methodology, we focus on a popular model of spreading over
networks: the susceptible-infected-susceptible (SIS) networked model. In the
standard version of this model, individuals informed about a piece of
information transmit this piece to its neighbors at an exponential rate. In
this paper, we extend this model to the case of transmission rates following a
phase-type distribution. Using this extended model, we analyze the dynamics of
the spread based on a vectorial representations of phase-type distributions. We
illustrate our results by analyzing spreading processes over networks with
transmission and recovery rates following a Weibull distribution
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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