Stochastic Optimal Control of Epidemic Processes in Networks

TL;DR

This paper introduces a novel stochastic optimal control framework for epidemic processes on networks, leveraging marked temporal point processes and SDEs with jumps to improve disease outbreak management using detailed data.

Contribution

It presents a new approach combining stochastic control and point processes to enhance epidemic intervention strategies, addressing limitations of existing methods.

Findings

Control strategy outperforms alternatives in synthetic data experiments

Method effectively utilizes fine-grained outbreak data

Provides a promising step towards practical data-driven epidemic control

Abstract

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.