Abstract
Transmission potential of a pathogen, often quantified by the time-varying reproduction number Rt, provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method, EpiMix, for Rt estimation, wherein we incorporated the impacts of exogenous factors and random effects under a Bayesian regression framework. Using Integrated Nested Laplace Approximation, EpiMix is able to efficiently generate reliable, deterministic Rt estimates. In the simulations and case studies performed, we further demonstrated the method's robustness in low-incidence scenarios, together with other merits, including its flexibility in selecting variables and tolerance of varying reporting rates. All these make EpiMix a potentially useful tool for real-time Rt estimation provided that the serial interval distribution, time series of case counts and external influencing factors are available.