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Evaluating Architectural Changes to Alter Pathogen Dynamics in a Dialysis Unit
Conference proceeding

Evaluating Architectural Changes to Alter Pathogen Dynamics in a Dialysis Unit

Hankyu Jang, Samuel Justice, Philip M Polgreen, Alberto M Segre, Daniel K Sewell and Sriram V Pemmaraju
2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp.961-968
08/2019
DOI: 10.1145/3341161.3343515

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Abstract

This paper presents a high-fidelity agent-based simulation of the spread of methicillin-resistant Staphylococcus aureus (MRSA), a serious hospital acquired infection, within the dialysis unit at the University of Iowa Hospitals and Clinics (UIHC). The simulation is based on ten days of fine-grained healthcare worker (HCW) movement and interaction data collected from a sensor mote instrumentation of the dialysis unit by our research group in the fall of 2013. The simulation layers a detailed model of MRSA pathogen transfer, die-off, shedding, and infection on top of agent interactions obtained from data. The specific question this paper focuses on is whether there are simple, inexpensive architectural or process changes one can make in the dialysis unit to reduce the spread of MRSA? We evaluate two architectural changes of the nurses' station: (i) splitting the central nurses' station into two smaller distinct nurses' stations, and (ii) doubling the surface area of the nursing station. The first architectural change is modeled as a graph partitioning problem on a HCW contact network obtained from our HCW movement data. Somewhat counter-intuitively, our results suggest that the first architectural modification and the resulting reduction in HCW-HCW contacts has little to noeffect on the spread of MRSA and may in fact lead to an increase in MRSA infection counts in some cases. In contrast, the second modification leads to a substantial reduction - between 12% and 22% for simulations with different parameters - in the number of patients infected by MRSA. These results suggest that the dynamics of an environmentally mediated infection such as MRSA may be quite different from that of infections whose spread is not substantially affected by the environment (e.g., respiratory infections or influenza).
 Epidemiology architecture change Biological system modeling Data models disease transmission environmental contamination healthcare-associated infections infection control Load modeling methicillin-resistant Staphylococcus aureus Pathogens Training Urban areas

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