Abstract
The basic reproduction number, R (0), and its real-time analogue, R(t) , are summary measures that reflect the ability of an infectious disease to spread through a population. Estimation methods for R(t) have a long history, have been widely developed and are now enhanced by application to the COVID-19 pandemic. While retrospective analyses of R(t) have provided insight into epidemic dynamics and the effects of control strategies in prior outbreaks, misconceptions around the interpretation of R(t) have arisen with broader recognition and near real-time monitoring of this parameter alongside reported case data during the COVID-19 pandemic. Here, we discuss some widespread misunderstandings regarding the use of R(t) as a barometer for population risk and its related use as an 'on/off' switch for policy decisions regarding relaxation of non-pharmaceutical interventions. Computation of R(t) from downstream data (e.g. hospitalizations) when infection counts are unreliable exacerbates lags between when transmission happens and when events are recorded. We also discuss analyses that have shown various relationships between R(t) and measures of mobility, vaccination coverage and a test-trace-isolation intervention in different settings.