Bayesian inference of reproduction number from epidemiological and genetic data using particle MCMC

Abstract Inference of the reproduction number through time is of vital importance during an epidemic outbreak. Typically, epidemiologists tackle this using observed prevalence or incidence data. However, prevalence and incidence data alone are often noisy or partial. Models can also have identifiability issues with determining whether a large amount of a small epidemic or a small amount of a large epidemic has been observed. Sequencing data however are becoming more abundant, so approaches which can incorporate genetic data are an active area of research. We propose using particle MCMC methods to infer the time-varying reproduction number from a combination of prevalence data reported at a set of discrete times and a dated phylogeny reconstructed from sequences. We validate our approach on simulated epidemics with a variety of scenarios. We then apply the method to real datasets of HIV-1 in North Carolina, USA and tuberculosis in Buenos Aires, Argentina. The models and algorithms are implemented in an open source R package called EpiSky which is available at https://github.com/alicia-gill/EpiSky.