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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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Time-Dependent Size-Resolved Bacterial and Fungal Aerosols in Beijing Subway

TL;DR: Wang et al. as discussed by the authors investigated time-dependent bioaerosol size distributions in Beijing subway system between March and April, 2015, and found that weekday microbial aerosol results exhibited strong time dependence with higher bacterial and fungal aerosol levels up to 2083 CFU m−3 and 483 CFUm−3 observed, respectively, for the peak hours.
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Dilution-based Evaluation of Airborne Infection Risk - Thorough Expansion of Wells-Riley Model

TL;DR: Experiments demonstrate that the dilution-based evaluation method proposed effectively evaluates the airborne infection risk both spatially and temporally, and indicates that the method proposed is a thorough expansion of the Wells-Riley model for evaluation of airborne infectionrisk with both spatial and temporal resolutions.
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Local characteristics of cross-unit contamination around high-rise building due to wind effect: mean concentration and infection risk assessment.

TL;DR: The characteristics of hazardous gas dispersion and possible cross-unit contamination around a complex-shaped high-rise residential building due to wind effect are thoroughly studied using physical modeling method for the purpose of prevention and control of accidental infectious diseases outbreak.
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Airborne spread of infectious SARS-CoV-2: Moving forward using lessons from SARS-CoV and MERS-CoV.

TL;DR: Higher temperatures and high relative humidity can have a modest effect on SARS-CoV-2 viability in the environment, as reported in previous studies to this date, but these studies do not support the fact that the virus has efficiently spread in the tropical regions of the globe.
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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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