An efficient approach to nowcasting the time-varying reproduction number

Estimating the instantaneous reproduction number ( ℛ t ) in near real-time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. A fast and flexible Bayesian methodology is proposed to overcome this challenge by estimating ℛ t while taking into account reporting delays. Furthermore, the uncertainty associated with the nowcasting of cases is naturally taken into account to get a valid uncertainty estimation of the nowcasted reproduction number. The proposed methodology is evaluated through a simulation study and applied to COVID-19 incidence data in Belgium.