A data-driven mathematical model of multi-drug resistant Acinetobacter baumannii transmission in an intensive care unit.

Abstract

Major challenges remain when attempting to quantify and evaluate the impacts of contaminated environments and heterogeneity in the cohorting of health care workers (HCWs) on hospital infections. Data on the detection rate of multidrug-resistant Acinetobacter baumannii (MRAB) in a Chinese intensive care unit (ICU) were obtained to accurately evaluate the level of environmental contamination and also to simplify existing models. Data-driven mathematical models, including mean-field and pair approximation models, were proposed to examine the comprehensive effect of integrated measures including cohorting, increasing nurse-patient ratios and improvement of environmental sanitation on MRAB infection. Our results indicate that for clean environments and with strict cohorting, increasing the nurse-patient ratio results in an initial increase and then a decline in MRAB colonization. In contrast, in contaminated environments, increasing the nurse-patient ratio may lead to either a consistent increase or an initial increase followed by a decline of MRAB colonization, depending on the level of environmental contamination and the cohorting rate. For developing more effective control strategies, the findings suggest that increasing the cohorting rate and nurse-patient ratio are effective interventions for relatively clean environments, while cleaning the environment more frequently and increasing hand washing rate are suitable measures in contaminated environments.