  In this work we demonstrate how to automate parts of the infectious
disease-control policy-making process via performing inference in existing
epidemiological models. The kind of inference tasks undertaken include
computing the posterior distribution over controllable, via direct
policy-making choices, simulation model parameters that give rise to acceptable
disease progression outcomes. Among other things, we illustrate the use of a
probabilistic programming language that automates inference in existing
simulators. Neither the full capabilities of this tool for automating inference
nor its utility for planning is widely disseminated at the current time. Timely
gains in understanding about how such simulation-based models and inference
automation tools applied in support of policymaking could lead to less
economically damaging policy prescriptions, particularly during the current
COVID-19 pandemic.
