Screening for an Infectious Disease as a Problem in Stochastic Control
This work addresses the challenge of efficient disease screening for public health, but it is incremental as it applies an existing method (Thompson sampling) to a new domain.
The paper tackles the problem of screening populations for infectious diseases by modeling it as a stochastic control problem, showing that while finding the optimum policy is difficult, Thompson sampling provides provably optimal Bayesian regret guarantees, with potential applications to diseases like COVID-19.
There has been much recent interest in screening populations for an infectious disease. Here, we present a stochastic-control model, wherein the optimum screening policy is provably difficult to find, but wherein Thompson sampling has provably optimal performance guarantees in the form of Bayesian regret. Thompson sampling seems applicable especially to diseases, for which we do not understand the dynamics well, such as to the super-spreading COVID-19.