Abstract

Surveillance data from an oncology hospital unit on Vancomycin-resistant enterococcus (VRE), one of the most prevalent and dangerous pathogens involved in hospital infections, is used to motivate possibilities of modeling nosocomial infection dynamics. This is done in the context of hospital monitoring and isolation procedures as a prelude to the evaluation and improved design of control measures. A discrete event delay differential equation model in conjunction with statistical computational methods is formulated to estimate key population-level nosocomial transmission parameters and isolation procedures. This framework is used to test the surveillance data's usefulness in model validation. In the process of model calibration we discovered significant irregularities in the available surveillance data; these irregularities are most likely the result of the data observational recording-process as well as those in the isolation procedures. Efforts to fit data within our highly flexible dynamic-modeling framework suggest that clinical-trial level surveillance data is needed if one is to successfully develop quantitative models for disease transmission and intervention. It is concluded that typical “cold” data sets typically encountered in biological/sociological quantitative modeling efforts may be inadequate for support of serious model development.