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Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus

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TLDR
Airborne spread of the virus appears to explain this large community outbreak of SARS in Hong Kong, and future efforts at prevention and control must take into consideration the potential for airborne spread of this virus.
Abstract
background There is uncertainty about the mode of transmission of the severe acute respiratory syndrome (SARS) virus. We analyzed the temporal and spatial distributions of cases in a large community outbreak of SARS in Hong Kong and examined the correlation of these data with the three-dimensional spread of a virus-laden aerosol plume that was modeled using studies of airflow dynamics. methods We determined the distribution of the initial 187 cases of SARS in the Amoy Gardens housing complex in 2003 according to the date of onset and location of residence. We then studied the association between the location (building, floor, and direction the apartment unit faced) and the probability of infection using logistic regression. The spread of the airborne, virus-laden aerosols generated by the index patient was modeled with the use of airflow-dynamics studies, including studies performed with the use of computational fluid-dynamics and multizone modeling. results The curves of the epidemic suggested a common source of the outbreak. All but 5 patients lived in seven buildings (A to G), and the index patient and more than half the other patients with SARS (99 patients) lived in building E. Residents of the floors at the middle and upper levels in building E were at a significantly higher risk than residents on lower floors; this finding is consistent with a rising plume of contaminated warm air in the air shaft generated from a middle-level apartment unit. The risks for the different units matched the virus concentrations predicted with the use of multizone modeling. The distribution of risk in buildings B, C, and D corresponded well with the three-dimensional spread of virus-laden aerosols predicted with the use of computational fluiddynamics modeling. conclusions Airborne spread of the virus appears to explain this large community outbreak of SARS, and future efforts at prevention and control must take into consideration the potential for airborne spread of this virus.

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Insights from the association of SARS-CoV S-protein with its receptor, ACE2.

TL;DR: Biochemical and functional studies of animal ACE2 and of the Sars-CoV spike (S) protein from SARS- CoV isolated from potential sources of SARS -CoV shed some light on the origin of the virus and perhaps the severity of disease caused in 2002–2003.
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The highly cited SARS research literature.

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Modeling infection risk and energy use of upper-room Ultraviolet Germicidal Irradiation systems in multi-room environments

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References
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Book

Computational methods for fluid dynamics

TL;DR: This text develops and applies the techniques used to solve problems in fluid mechanics on computers and describes in detail those most often used in practice, including advanced techniques in computational fluid dynamics.
Journal ArticleDOI

A note on a general definition of the coefficient of determination

TL;DR: In this article, a generalization of the coefficient of determination R2 to general regression models is discussed, and a modification of an earlier definition to allow for discrete models is proposed.
Journal ArticleDOI

A Major Outbreak of Severe Acute Respiratory Syndrome in Hong Kong

TL;DR: SARS is a serious respiratory illness that led to significant morbidity and mortality in this cohort of 138 cases of suspected SARS during a hospital outbreak in Hong Kong.
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Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study.

TL;DR: The consistent clinical progression, shifting radiological infiltrates, and an inverted V viral-load profile suggest that worsening in week 2 is unrelated to uncontrolled viral replication but may be related to immunopathological damage.
Book

Modelling Binary Data

David Collett
TL;DR: In this article, the authors describe the use of statistical software for measuring the success probability of binary response probability in the presence of exposure and disease in the context of binary time series.
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