Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-18T18:48:46.666Z Has data issue: false hasContentIssue false
  • English
  • Français

Mathematical Modeling of Pathogen Trajectory in a Patient Care Environment

Part of: BARRY FARR AWARD RECIPIENTS

Published online by Cambridge University Press:  02 January 2015

Angela L. Hewlett ,
Scott E. Whitney ,
Shawn G. Gibbs ,
Philip W. Smith  and
Hendrik J. Viljoen
Angela L. Hewlett*
Affiliation:
Department of Internal Medicine, Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, Nebraska
Scott E. Whitney
Affiliation:
Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, Nebraska
Shawn G. Gibbs
Affiliation:
Department of Environmental, Agricultural, and Occupational Health, University of Nebraska Medical Center, Omaha, Nebraska
Philip W. Smith
Affiliation:
Department of Internal Medicine, Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, Nebraska
Hendrik J. Viljoen
Affiliation:
Department of Chemical and Biomolecular Engineering, University of Nebraska, Lincoln, Nebraska
*
985400 Nebraska Medical Center, Omaha, NE 68198 (alhewlett@unmc.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

Minimizing healthcare worker exposure to airborne infectious pathogens is an important infection control practice. This study utilized mathematical modeling to evaluate the trajectories and subsequent concentrations of particles following a simulated release in a patient care room.

Design.

Observational study.

Setting.

Biocontainment unit patient care room at a university-affiliated tertiary care medical center.

Methods

. Quantitative mathematical modeling of airflow in a patient care room was achieved using a computational fluid dynamics software package. Models were created on the basis of a release of particles from various locations in the room. Computerized particle trajectories were presented in time-lapse fashion over a blueprint of the room. A series of smoke tests were conducted to visually validate the model.

Results.

Most particles released from the head of the bed initially rose to the ceiling and then spread across the ceiling and throughout the room. The highest particle concentrations were observed at the head of the bed nearest to the air return vent, and the lowest concentrations were observed at the foot of the bed.

Conclusions.

Mathematical modeling provides clinically relevant data on the potential exposure risk in patient care rooms and is applicable in multiple healthcare delivery settings. The information obtained through mathematical modeling could potentially serve as an infection control modality to enhance the protection of healthcare workers.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 11 , November 2013 , pp. 1181 - 1188
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Smith, PW, Anderson, AO, Christopher, GW, et al. Designing a biocontainment unit to care for patients with serious communicable diseases: a consensus statement. Biosecur Bioterror 2006;(4):351–365.Google Scholar
2.Brouqui, P, Puro, V, Fusco, FM, et al. Infection control in the management of highly pathogenic infectious diseases: consensus of the European Network of Infectious Disease. Lancet Infect Dis 2009;9(5):301–311.Google Scholar
3.Fennelly, KP, Nardell, EA. The relative efficacy of respirators and room ventilation in preventing occupational tuberculosis. Infect Control Hosp Epidemiol 1998;19:754–759.Google Scholar
4.Memarzadeh, F, Manning, AP. Comparison of operating room ventilation systems in the protection of the surgical site. ASHRAE Trans 2002;108:1–13.Google Scholar
5.Xie, X, Chwang, AT, Ho, PL, Seto, WH. How far droplets can move in indoor environments: revisiting the Wells evaporation-falling curve. Indoor Air 2007;17(3):211–225.Google Scholar
6.Singh, V, Chowdhary, R, Chowdhary, N. The role of cough and hyperventilation in perpetuating airway inflammation in asthma. J Assoc Physicians India 2000;48(3):343–345.Google Scholar
7.Pavelchak, N, DePersis, RP, London, Met al. Identification of factors that disrupt negative air pressurization of respiratory isolation rooms. Infect Control Hosp Epidemiol 2000;21(3):191–195.Google Scholar
8.Woods, JN, McKarns, JS. Evaluation of capture efficiencies of large push-pull ventilation systems with both visual and tracer techniques. Am Ind Hyg Assoc J 1995;56(12):1208–1214.Google Scholar
9.Siegel, JD, Rhinehart, E, Jackson, M, Chiarello, L; Healthcare Infection Control Practices Advisory Committee. 2007 Guideline for Isolation Precautions: Preventing Transmission of Infectious Agents in Healthcare Settings. Atlanta: Centers for Disease Control and Prevention, 2007. http://www.cdc.gov/hicpac/pdf/isolation/Isolation2007.pdf. Accessed December 21, 2011.Google Scholar
10.Rice, N, Streifel, A, Vesley, D. An evaluation of hospital special-ventilation-room pressures. Infect Control Hosp Epidemiol 2001;22(1):19–23.Google Scholar
11.Adams, NJ, Johnson, DL, Lynch, RA. The effect of pressure differential and care provider movement on airborne infectious isolation room containment effectiveness. Am J Infect Control 2011;39(2):91–97.Google Scholar
12.Saravia, SA, Raynor, PC, Streifel, AJ. A performance assessment of airborne infection isolation rooms. Am J Infect Control 2007; 35:324–331.Google Scholar