Estimating nosocomial transmission of micro-organisms in hospital settings using patient records and culture data

Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020-2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus (both sensitive, MSSA, and resistant, MRSA, phenotypes), Enterococcus faecium and Enterococcus faecalis. We estimate that nosocomial transmission for E. coli is negligible. While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except E. coli. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.