Distributed Resource Allocation for Epidemic control
This work addresses the problem of decentralized epidemic control for networked populations, but the approach is incremental as it applies existing optimization methods to a known model.
The paper proposes a distributed resource allocation strategy using D-ADMM to control epidemic outbreaks in networked populations, formulated as a geometric program with spectral constraints, and demonstrates its effectiveness through simulations.
We present a distributed resource allocation strategy to control an epidemic outbreak in a networked population based on a Distributed Alternating Direction Method of Multipliers (D-ADMM) algorithm. We consider a linearized Susceptible- Infected-Susceptible (SIS) epidemic spreading model in which agents in the network are able to allocate vaccination resources (for prevention) and antidotes (for treatment) in the presence of a contagion. We express our epidemic control condition as a spectral constraint involving the Perron-Frobenius eigenvalue, and formulate the resource allocation problem as a Geometric Program (GP). Next, we separate the network-wide optimization problem into subproblems optimally solved by each agent in a fully distributed way. We conclude the paper by illustrating performance of our solution framework with numerical simulations.