Comparing methods to estimate time-varying reproduction numbers using genomic and epidemiological data

Estimating the time-varying reproduction number Rt during an epidemic is important. Rt indicates whether an epidemic is growing or declining and can aid in assessing the impact of interventions. Recent advances have enhanced methods for estimating Rt and other epidemiological parameters from surveillance and genomic data independently. The Birth-Death Skyline (BDSKY) in BEAST 2.5 and EpiEstim are two common methods used to estimate Rt from these data sources. We introduce an outbreak simulation platform that generates pathogen sequence data and epidemiological linelists. We use this platform to to assess Rt estimation methods' accuracy under various sampling scenarios similar to what was observed during past epidemics. We identified biases and determined appropriate scenarios for improving the accuracy of Rt estimation approaches based on multiple outbreak simulations. When data becomes sparse and unreliable, genomic sequence data provide reasonable Rt estimates even when sampling is not uniform.