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Original Articles

Development of an Empirical Model to Aid in Designing Airborne Infection Isolation Rooms

, &
Pages 198-207 | Published online: 31 Oct 2007
 

Abstract

Airborne infection isolation rooms (AIIRs) house patients with tuberculosis, severe acute respiratory syndrome (SARS), and many other airborne infectious diseases. Currently, faci-lity engineers and designers of heating, ventilation, and air-conditioning (HVAC) systems have few analytical tools to estimate a room's leakage area and establish an appropriate flow differential (ΔQ) in hospitals, shelters, and other facilities where communicable diseases are present. An accurate estimate of leakage area and selection of ΔQ is essential for ensuring that there is negative pressure (i.e., pressure differential [ΔP]) between an AIIR and adjoining areas. National Institute for Occupational Safety and Health (NIOSH) researchers evaluated the relationship between ΔQ and ΔP in 67 AIIRs across the United States and in simulated AIIR. Data gathered in the simulated AIIR was used to develop an empirical model describing the relationship between ΔQ, ΔP, and leakage area. Data collected in health care facilities showed that the model accurately predicted the leakage area 44 of 48 times. Statistical analysis of the model and experimental validation showed that the model effectively estimated the actual leakage area from −39% to +22% with 90% confidence. The NIOSH model is an effective, cost-cutting tool that can be used by HVAC engineers and designers to estimate leakage area and select an appropriate ΔQ in AIIRs to reduce the airborne transmission of disease.

Acknowledgments

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health (NIOSH). Mention of company names or products does not constitute endorsement by the Centers for Disease Control and Prevention (CDC), NIOSH.

Notes

A See also and .

B Approximate.

A As developed in the statistical evaluation of the model.

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