Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission
Eyre, D. W., Laager, M., Walker, A. S., Cooper, B. S. and D. J. Wilson on behalf of the CDC Modeling Infectious Diseases in Healthcare Program (MInD-Healthcare) (2020)
PLoS Computational Biology 17: e1008417 (pdf)
Probabilistic transmission models can offer detailed insights into the transmission of healthcare-associated infections, which in turn can be used to inform infection control and improve patient outcomes. Pathogen whole-genome sequencing may improve the precision of our understanding of transmission. However, to date, most stochastic transmission models using sequencing data have focused on relatively small datasets and studied specific outbreaks. Conversely, large-scale sequencing studies of endemic infections have mostly relied on simple rules for identifying or excluding plausible transmission events on the basis of genetic data. Here we present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data, using a coalescent theory-based framework to assess the probability of transmission associated with pairwise single nucleotide polymorphism differences between genomes from cases.
We apply our model to study the transmission of endemic healthcare-associated Clostridioides difficile using a dataset of >1200 infections. C. difficile is the leading cause of infectious diarrhoea in hospital inpatients and a cause of substantial morbidity and mortality. Our stochastic models provide new insights into the mechanisms underlying the success of healthcare-associated strains, including enhanced transmission and environmental persistence. We are also able to demonstrate variation in transmission between hospitals, medical specialties and over time. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to a variety of healthcare associated infections and our findings have important implications for effective control of C. difficile.