GIScience 2012, Columbus, Ohio, 18-21 September 2012
S. Copeland and L. Bian
“In the past decade, communicable diseases have been at the forefront of global health concerns. H1N1, SARS, and Avian Influenza have each threatened global welfare and challenged public health understandings of communicable disease containment and eradication. Communicable diseases are transmitted via Close Proximity Interactions (CPIs) between infectious and susceptible persons (Salathe et al. 2010). Most CPIs occur in indoor environments such as homes, offices, schools, shopping malls, and within health facilities such as hospitals. CPIs are inherently a spatially and temporally dynamic interactive process. Research concerning CPIs in indoor environments is still in its infancy (Salathe et al 2010;
Stehle et al 2011; Stehle et al. 2011b). Consequently, although it is acknowledged that indoor environments mediate person to person interactions, our understanding of the interplay between the spatial structure of indoor environments and CPIs is limited.
“To address these challenges, this research simulates assigned seating interventions targeting CPIs among patients with and without influenza in a hospital waiting room. The hospital waiting room is critical to the intended simulation for two reasons. First, there is evidence from past outbreaks, such as SARS, that hospital environments may play a vital role in epidemic and pandemic outbreaks. Second, the waiting room is representative of complex indoor environments and provides a testbed for understanding individual interactions in structured environments. The simulated seating interventions invoke partitioning between influenza symptomatic and asymptomatic patients; partitioning has been proposed by the Department of Health and Human Services (2007) and the Centers for Disease Control and Prevention (2008) in response to potential outbreaks in hospital environments. Two preliminary results are presented. First, CPIs in the waiting room are described using social network analysis; second, the impact of partitioning on influenza transmission in the waiting room is shown using descriptive statistics.”
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