Contact networks and infection prevention

Most pathogen transmission in healthcare facilities occurs via close contact, whether between healthcare personnel (HCP) or between HCP and patients. It is common sense that when it comes to transmission risk, all HCP are not equal. Consider the ICU nurse assigned to two patients during his shift, rarely venturing away from these two bedsides, versus the ID fellow or the respiratory therapist who contacts many patients across several units. Yet many models of disease transmission either don’t take contact network epidemiology into account, or don’t have sufficient data to understand the properties of HCP contact networks.

Our colleague Phil Polgreen and his collaborators in Iowa’s computational epidemiology group have constructed HCP contact networks using electronic medical record logins, validated the data using wireless sensors in our MICU, and applied the data to model the impact of various strategies to vaccination that focus on random application versus applying the intervention to HCP based upon degree (number) of contacts or distance (mobility) in the hospital. The figure below, from their recently published PLoS One paper, demonstrates the impact in a particular contact network (a) of vaccinating randomly (b), versus based upon degree (c) or distance (d).

This work has important implications for infection prevention practice. Imagine a year in which influenza vaccination is in short supply—see below for the impact on the disease attack rates in hypothetical scenarios where vaccination is based upon degree of contacts or mobility (distance) versus random allocation, in a sparse (a) or dense (b) contact network.