Abstract
To create an affordable and accurate method for continuously monitoring bacterial transmission rates in healthcare settings. We present a discrete simulation model that relies on the relationship between in-hospital transmission rates and strain diversity. We also present a proof of concept application of this model to a prospective molecular epidemiology data set to estimate transmission rates for Pseudomonas aeruginosa and Staphylococcus aureus. Inpatient units of an academic referral center. All inpatients with nosocomial infections. Mathematical model to estimate transmission rates. Maximum likelihood estimates for transmission rates of these two species on different hospital units ranged from 0 to 0.36 transmission event per colonized patient per day. This approach is feasible, although estimates of transmission rates based solely on strain typed clinical cultures may be too imprecise for routine use in infection control. A modest level of surveillance sampling substantially improves the estimation accuracy.
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