This study discusses methods by which we can estimate the size of an outbreak from various surveillance measures.

Policymakers dealing with COVID-19 will need to decide when to switch from measures that contain and eliminate the outbreak to measures designed to mitigate its effects. Because surveillance systems cannot capture all cases, the state of the epidemic is never really known. The need for timely estimates id ongoing. This article provides a Bayesian methodology to estimate outbreak size from limited surveillance systems like virologic testing of pneumonia cases or samples from a network of general practitioners.