Estimating epidemic dynamics with genomic and time series data

Accurately estimating the prevalence and transmissibility of an infectious disease is an important task in genetic infectious disease epidemiology. However, generating accurate estimates of these quantities, that are informed by both epidemic time series and pathogen genome sequence data, is a challenging problem. While birth-death processes and coalescent-based models are popular methods for modelling the transmission of infectious diseases, they both struggle (for different reasons) with estimating the prevalence of infection. Here we extended our approximate likelihood, which combines phylogenetic information from sampled pathogen genomes and epidemiological information from a time series of case counts, to estimate historical prevalence (in addition to the effective reproduction number). We implement this new method in a BEAST2 package called Timtam. In a simulation study our approximation is well-calibrated and can recover the parameters of simulated data. To demonstrate how Timtam can be applied to real data sets we carried out empirical analyses of data from two infectious disease outbreaks: the outbreak of SARS-CoV-2 onboard the Diamond Princess cruise ship in early 2020 and poliomyelitis in Tajikistan in 2010. In both cases we recover estimates consistent with previous analyses.