Spatial Distribution of Infection Risk of SARS Transmission in a Hospital Ward

Hua Qian, Yuguo Li, Peter V. Nielsen, Xinhua Huang

Research output: Contribution to journalJournal articleResearchpeer-review

84 Citations (Scopus)

Abstract

The classical Wells-Riley model for predicting risk of airborne transmission of diseases assumes a uniform spatial distribution of the infected cases in an enclosed space. A new mathematical model is developed here for predicting the spatial distribution of infection risk of airborne transmitted diseases by integrating the Wells-Riley equation into computational fluid dynamics. We applied our new integrated model to analyze a large nosocomial SARS outbreak in Hong Kong during the 2003 SARS epidemics, which was studied in the literature with regard to the association between airflow and SARS infection.

The predicted numbers of infected cases of medical students in the same cubicle, the adjacent cubicle and the distant cubicle were 6.39, 0.78 and 0.2 respectively while the observed numbers of infected medical students in the three cubicles were 7, 0 and 0 respectively during the morning of March 6th, which was the highest attack period. The predicted numbers of infected cases of inpatients during the morning of March 6th in the same cubicle, the adjacent cubic and the distance cubicle were 7.8, 5.1, and 4.8 respectively which also agree well with the observed distribution of the infected inpatients during the entire infection period.

The new developed model provides a new modelling tool for investigating the airborne transmission of diseases in enclosed spaces. The model is applicable when the susceptible stays mostly at the same location in an enclosed space during the infectious period, such as inpatients in a hospital ward, passengers in an airplane etc.
Original languageEnglish
JournalBuilding and Environment
Volume44
Issue number8
Pages (from-to)1651-1658
ISSN0360-1323
DOIs
Publication statusPublished - 2009

Keywords

  • Wells–Riley equation
  • SARS
  • Airborne transmission of diseases
  • CFD

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