Transient Dynamics of Infection Transmission in a Simulated Intensive Care Unit

TL;DR

This study investigates the transient dynamics of MRSA transmission in ICUs using different models, revealing that infection rates depend on transient behaviors rather than long-term equilibria.

Contribution

It introduces and compares three models of ICU infection transmission, highlighting the importance of transient dynamics in understanding infection spread.

Findings

Meta-population model's R0 was 0.278

Single-staff model's R0 was 0.337

Transient dynamics influence infection rates more than equilibrium states

Abstract

Healthcare-associated infections (HAIs) remain a public health problem. Previous work showed intensive care unit (ICU) population structure impacts methicillin-resistant Staphylococcus aureus (MRSA) rates. Unexplored in that work was the transient dynamics of this system. We consider the dynamics of MRSA in an ICU in three different models: 1) a Ross-McDonald model with a single healthcare staff type, 2) a Ross-McDonald model with nurses and doctors considered as separate populations and 3) a meta-population model that segments patients into smaller groups seen by a single nurse. The basic reproduction number, R0 is derived using the Next Generation Matrix method, while the importance of the position of patients within the meta-population model is assessed via stochastic simulation. The single-staff model had an R0 of 0.337, while the other two models had R0s of 0.278. The meta-population model's R0 was not sensitive to the time nurses spent with their assigned patients vs. unassigned patients. This suggests previous results showing that simulated infection rates are dependent on this parameter are the result of differences in the transient dynamics between the models, rather than differing long-term equilibria.