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

Estimating the time-varying reproduction number ℛ _t during an epidemic is important. ℛ _t indicates whether an epidemic is growing or declining and can aid in assessing the impact of interventions. Recent advances have enhanced methods for estimating ℛ _t 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 ℛ _t 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 ℛ _t 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 ℛ _t estimation approaches based on multiple outbreak simulations. When data becomes sparse and unreliable, genomic sequence data provide reasonable ℛ _t estimates even when sampling is not uniform.