Sequential Dynamic Resource Allocation for Epidemic Control

TL;DR

This paper introduces a novel sequential decision-making framework for dynamic resource allocation in epidemic control, accounting for limited network access and applying sequential selection algorithms to improve intervention strategies.

Contribution

It extends the standard DRA model to include partial network access and proposes the SDRA model inspired by the Secretary Problem, offering a new approach to epidemic intervention strategies.

Findings

Sequential selection algorithms improve resource allocation effectiveness.

Partial network access models are more realistic for real-world scenarios.

Simulation results demonstrate the advantages of SDRA over traditional methods.

Abstract

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.