Sequential Dynamic Resource Allocation for Epidemic Control
This work addresses epidemic control for public health administrators by extending dynamic resource allocation models to be more realistic, though it is incremental as it builds on existing DRA frameworks.
The paper tackles the problem of controlling epidemics under real-world constraints by introducing a Sequential Dynamic Resource Allocation (SDRA) model that restricts access to a fraction of network nodes per round and incorporates sequential decision-making, showing performance comparisons in SIS epidemic simulations.
Under the Dynamic Resource Allocation (DRA) model, an administrator has the mission to allocate dynamically a limited budget of resources to the nodes of a network in order to reduce a diffusion process (DP) (e.g. an epidemic). The standard DRA assumes that the administrator has constantly full information and instantaneous access to the entire network. Towards bringing such strategies closer to real-life constraints, we first present the Restricted DRA model extension where, at each intervention round, the access is restricted to only a fraction of the network nodes, called sample. Then, inspired by sequential selection problems such as the well-known Secretary Problem, we propose the Sequential DRA (SDRA) model. Our model introduces a sequential aspect in the decision process over the sample of each round, offering a completely new perspective to the dynamic DP control. Finally, we incorporate several sequential selection algorithms to SDRA control strategies and compare their performance in SIS epidemic simulations.