Stochastic Optimal Control of Epidemic Processes in Networks
This work addresses the problem of developing more effective epidemic control strategies for public health applications, representing an incremental advancement with a novel methodological perspective.
The authors tackled the problem of controlling SIS epidemic processes in networks by developing a novel stochastic optimal control framework using marked temporal point processes and SDEs with jumps, which consistently outperformed several alternative strategies in preliminary synthetic experiments.
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.