Fast hospital discharge rates blur within-hospital ‘transmission footprint’ in bacterial genomes, as showcased with Staphylococcus aureus

Abstract

The relatively slow mutation rates of bacterial pathogens impose severe limitations on phylodynamic analysis of bacterial outbreaks. However, whole-genome sequencing may enable accurate inference of bacterial transmission dynamics in health-care settings. We simulated the epidemic dynamics of a Staphylococcus aureus lineage using a stochastic model with a hospital and community compartment connected by patient admission and discharge. We generated synthetic genomic sequences and performed Bayesian phylodynamic inference on a proportion of samples from each simulated outbreak. When samples are obtained from both compartments, hospital transmission rate (λH) and community transmission rate (λC) are accurately estimated, if λH is on the same scale as the discharge rate. If λH is substantially lower than the discharge rate, a robust quantification of within-hospital transmission dynamics is challenging. Excluding samples from the community resulted in a notable underestimation of λH when λH ≥ λC. When transmission was ‘community-driven’, but sampling was restricted to hospital cases only, estimates are closer to the true λH, if hospital sampling proportion is known. Otherwise, λH estimates reflected the transmission dynamics within the community. When using genomic data to estimate bacterial transmission rates in a health-care setting, it is essential to take into account the surrounding community. Many infections related to nosocomial outbreaks will not be observed within the hospital due to fast discharge rates. In the absence of usable genomic data from the community, alternative estimates of community transmission rates from publicly available data should be incorporated. Transmission rate estimates from nosocomial genomes alone need to be interpreted with care.