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

## TL;DR

Fast patient discharge rates can obscure the true spread of bacteria like Staphylococcus aureus in hospitals, making it hard to track infections without community data.

## Contribution

The study introduces a Bayesian phylodynamic approach to better understand bacterial transmission in healthcare settings using genomic data.

## Key findings

- Hospital transmission rates are hard to estimate accurately if discharge rates are much higher than transmission rates.
- Excluding community samples leads to underestimation of hospital transmission rates.
- Community-driven transmission can be misinterpreted as hospital-based if community data are missing.

## 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.

In our research, we explored how integrating evolutionary biology and epidemiology through a Bayesian phylodynamic inference method can improve our understanding and management of bacterial outbreaks. While this approach has been widely used for viruses, its application to bacterial pathogens, such as Staphylococcus aureus, has been more limited. S. aureus is a common human pathogen responsible for numerous infections in both community and healthcare settings, making it a significant public health concern globally.

Our simulation study aimed to assess the effectiveness of phylodynamic methods in investigating S. aureus-like outbreaks. We simulated various scenarios to evaluate how well phylodynamic analysis can track transmission dynamics within hospitals and surrounding communities. Our findings suggest that when bacterial genetic data from both hospital patients and the community are available, phylodynamics provides valuable insights into bacterial spread. However, the effectiveness of these methods diminishes significantly when only hospital data are considered. This highlights the importance of incorporating data from both sources to gain a comprehensive understanding of bacterial transmission in healthcare settings. Our study lays the groundwork for more effective strategies in monitoring and responding to bacterial outbreaks.