Title: Stochastic Optimal Control of Epidemic Processes in Networks,
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.