Optimal Resource Allocation for Control of Networked Epidemic Models
Cameron Nowzari, Victor M. Preciado, George Pappas

Citation
Cameron Nowzari, Victor M. Preciado, George Pappas. "Optimal Resource Allocation for Control of Networked Epidemic Models". IEEE Transactions on Control of Network Systems, 2015.

Abstract
This work proposes and analyzes a generalized epidemic model over arbitrary directed graphs with heterogeneous nodes. The proposed model, called the Generalized Susceptible-Exposed-Infected-Vigilant (G-SEIV), subsumes a large number of popular epidemic models considered in the literature as special cases. Using a mean-field approximation, we derive a set of ODEs describing the spreading dynamics, provide a careful analysis of the disease-free equilibrium, and derive necessary and sufficient conditions for global exponential stability. Building on this analysis, we consider the problem of containing an initial epidemic outbreak under budget constraints. More specifically, we consider a collection of control actions (e.g. administering vaccines/antidotes, limiting the traffic between cities, or running awareness campaigns), for which we are given suitable cost functions. In this context, we develop an optimization framework to provide solutions for the following two allocation problems: (i) find the minimum cost required to eradicate the disease at a desired exponential decay rate, and (ii) given a fixed budget, find the resource allocation to eradicate the disease at the fastest possible exponential decay rate. Our technical approach relies on the reformulation of these problems as geometric programs that can be solved efficiently in polynomial time using tools from graph theory and convex optimization. In contrast with previous works, our optimization framework allows us to simultaneously allocate different types of control resources over heterogeneous populations under budget constraints. We illustrate our results through numerical simulations.

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  • HTML
    Cameron Nowzari, Victor M. Preciado, George Pappas. <a
    href="http://www.terraswarm.org/pubs/627.html"
    >Optimal Resource Allocation for Control of Networked
    Epidemic Models</a>, <i>IEEE Transactions on
    Control of Network Systems</i>,  2015.
  • Plain text
    Cameron Nowzari, Victor M. Preciado, George Pappas.
    "Optimal Resource Allocation for Control of Networked
    Epidemic Models". <i>IEEE Transactions on Control
    of Network Systems</i>,  2015.
  • BibTeX
    @article{NowzariPreciadoPappas15_OptimalResourceAllocationForControlOfNetworkedEpidemic,
        author = {Cameron Nowzari and Victor M. Preciado and George
                  Pappas},
        title = {Optimal Resource Allocation for Control of
                  Networked Epidemic Models},
        journal = {IEEE Transactions on Control of Network Systems},
        year = {2015},
        abstract = {This work proposes and analyzes a generalized
                  epidemic model over arbitrary directed graphs with
                  heterogeneous nodes. The proposed model, called
                  the Generalized
                  Susceptible-Exposed-Infected-Vigilant (G-SEIV),
                  subsumes a large number of popular epidemic models
                  considered in the literature as special cases.
                  Using a mean-field approximation, we derive a set
                  of ODEs describing the spreading dynamics, provide
                  a careful analysis of the disease-free
                  equilibrium, and derive necessary and sufficient
                  conditions for global exponential stability.
                  Building on this analysis, we consider the problem
                  of containing an initial epidemic outbreak under
                  budget constraints. More specifically, we consider
                  a collection of control actions (e.g.
                  administering vaccines/antidotes, limiting the
                  traffic between cities, or running awareness
                  campaigns), for which we are given suitable cost
                  functions. In this context, we develop an
                  optimization framework to provide solutions for
                  the following two allocation problems: (i) find
                  the minimum cost required to eradicate the disease
                  at a desired exponential decay rate, and (ii)
                  given a fixed budget, find the resource allocation
                  to eradicate the disease at the fastest possible
                  exponential decay rate. Our technical approach
                  relies on the reformulation of these problems as
                  geometric programs that can be solved efficiently
                  in polynomial time using tools from graph theory
                  and convex optimization. In contrast with previous
                  works, our optimization framework allows us to
                  simultaneously allocate different types of control
                  resources over heterogeneous populations under
                  budget constraints. We illustrate our results
                  through numerical simulations.},
        URL = {http://terraswarm.org/pubs/627.html}
    }
    

Posted by Christopher Brooks on 29 Sep 2015.
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