Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus
22 Apr 2004-The New England Journal of Medicine (Massachusetts Medical Society)-Vol. 350, Iss: 17, pp 1731-1739
TL;DR: 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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TL;DR: These analyses provide insights into the receptor usage, cell entry, host cell infectivity and animal origin of 2019-nCoV and may help epidemic surveillance and preventive measures against 2019- nCoV.
Abstract: Recently, a novel coronavirus (2019-nCoV) has emerged from Wuhan, China, causing symptoms in humans similar to those caused by severe acute respiratory syndrome coronavirus (SARS-CoV). Since the SARS-CoV outbreak in 2002, extensive structural analyses have revealed key atomic-level interactions between the SARS-CoV spike protein receptor-binding domain (RBD) and its host receptor angiotensin-converting enzyme 2 (ACE2), which regulate both the cross-species and human-to-human transmissions of SARS-CoV. Here, we analyzed the potential receptor usage by 2019-nCoV, based on the rich knowledge about SARS-CoV and the newly released sequence of 2019-nCoV. First, the sequence of 2019-nCoV RBD, including its receptor-binding motif (RBM) that directly contacts ACE2, is similar to that of SARS-CoV, strongly suggesting that 2019-nCoV uses ACE2 as its receptor. Second, several critical residues in 2019-nCoV RBM (particularly Gln493) provide favorable interactions with human ACE2, consistent with 2019-nCoV's capacity for human cell infection. Third, several other critical residues in 2019-nCoV RBM (particularly Asn501) are compatible with, but not ideal for, binding human ACE2, suggesting that 2019-nCoV has acquired some capacity for human-to-human transmission. Last, while phylogenetic analysis indicates a bat origin of 2019-nCoV, 2019-nCoV also potentially recognizes ACE2 from a diversity of animal species (except mice and rats), implicating these animal species as possible intermediate hosts or animal models for 2019-nCoV infections. These analyses provide insights into the receptor usage, cell entry, host cell infectivity and animal origin of 2019-nCoV and may help epidemic surveillance and preventive measures against 2019-nCoV.IMPORTANCE The recent emergence of Wuhan coronavirus (2019-nCoV) puts the world on alert. 2019-nCoV is reminiscent of the SARS-CoV outbreak in 2002 to 2003. Our decade-long structural studies on the receptor recognition by SARS-CoV have identified key interactions between SARS-CoV spike protein and its host receptor angiotensin-converting enzyme 2 (ACE2), which regulate both the cross-species and human-to-human transmissions of SARS-CoV. One of the goals of SARS-CoV research was to build an atomic-level iterative framework of virus-receptor interactions to facilitate epidemic surveillance, predict species-specific receptor usage, and identify potential animal hosts and animal models of viruses. Based on the sequence of 2019-nCoV spike protein, we apply this predictive framework to provide novel insights into the receptor usage and likely host range of 2019-nCoV. This study provides a robust test of this reiterative framework, providing the basic, translational, and public health research communities with predictive insights that may help study and battle this novel 2019-nCoV.
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TL;DR: The ability of hospital ventilation systems to filter Aspergillus and other fungi following a building implosion and the impact of bedside design and furnishing on nosocomial infections are investigated.
2,632 citations
Cites background from "Evidence of Airborne Transmission o..."
...101, [135] [136] [137] [138] [139] 149, 254 For example, exposure to aerosol-generating procedures (eg, endotracheal intubation, suctioning) has been associated with transmission of infection to large numbers of HCWs outside of the United States....
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...[134] [135] [136] [137] [138] [139] [140] [141] This is true of other infectious agents as well, such as influenza virus 130 and noroviruses....
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TL;DR: It is shown that contact tracing data from eight directly transmitted diseases shows that the distribution of individual infectiousness around R0 is often highly skewed, and implications for outbreak control are explored, showing that individual-specific control measures outperform population-wide measures.
Abstract: Population-level analyses often use average quantities to describe heterogeneous systems, particularly when variation does not arise from identifiable groups. A prominent example, central to our current understanding of epidemic spread, is the basic reproductive number, R(0), which is defined as the mean number of infections caused by an infected individual in a susceptible population. Population estimates of R(0) can obscure considerable individual variation in infectiousness, as highlighted during the global emergence of severe acute respiratory syndrome (SARS) by numerous 'superspreading events' in which certain individuals infected unusually large numbers of secondary cases. For diseases transmitted by non-sexual direct contacts, such as SARS or smallpox, individual variation is difficult to measure empirically, and thus its importance for outbreak dynamics has been unclear. Here we present an integrated theoretical and statistical analysis of the influence of individual variation in infectiousness on disease emergence. Using contact tracing data from eight directly transmitted diseases, we show that the distribution of individual infectiousness around R(0) is often highly skewed. Model predictions accounting for this variation differ sharply from average-based approaches, with disease extinction more likely and outbreaks rarer but more explosive. Using these models, we explore implications for outbreak control, showing that individual-specific control measures outperform population-wide measures. Moreover, the dramatic improvements achieved through targeted control policies emphasize the need to identify predictive correlates of higher infectiousness. Our findings indicate that superspreading is a normal feature of disease spread, and to frame ongoing discussion we propose a rigorous definition for superspreading events and a method to predict their frequency.
2,274 citations
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TL;DR: The concerted and coordinated response that contained SARS is a triumph for global public health and provides a new paradigm for the detection and control of future emerging infectious disease threats.
Abstract: The severe acute respiratory syndrome (SARS) is responsible for the first pandemic of the 21st century. Within months after its emergence in Guangdong Province in mainland China, it had affected more than 8000 patients and caused 774 deaths in 26 countries on five continents. It illustrated dramatically the potential of air travel and globalization for the dissemination of an emerging infectious disease and highlighted the need for a coordinated global response to contain such disease threats. We review the cause, epidemiology, and clinical features of the disease.
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TL;DR: Yet another pathogenic HCoV, 2019 novel coronavirus (2019-nCoV), was recognized in Wuhan, China, and has caused serious illness and death, and the ultimate scope and effect of this outbreak is unclear at present.
Abstract: Human coronaviruses (HCoVs) have long been considered inconsequential pathogens, causing the “common cold” in otherwise healthy people. However, in the 21st century, 2 highly pathogenic HCoVs—severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV)—emerged from animal reservoirs to cause global epidemics with alarming morbidity and mortality. In December 2019, yet another pathogenic HCoV, 2019 novel coronavirus (2019-nCoV), was recognized in Wuhan, China, and has caused serious illness and death. The ultimate scope and effect of this outbreak is unclear at present as the situation is rapidly evolving. Coronaviruses are large, enveloped, positivestrand RNA viruses that can be divided into 4 genera: alpha, beta, delta, and gamma, of which alpha and beta CoVs are known to infect humans.1 Four HCoVs (HCoV 229E, NL63, OC43, and HKU1) are endemic globally and account for 10% to 30% of upper respiratory tract infections in adults. Coronaviruses are ecologically diverse with the greatest variety seen in bats, suggesting that they are the reservoirs for many of these viruses.2 Peridomestic mammals may serve as intermediate hosts, facilitating recombination and mutation events with expansion of genetic diversity.
1,561 citations
References
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01 Jan 1996
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.
Abstract: Preface. Basic Concepts of Fluid Flow.- Introduction to Numerical Methods.- Finite Difference Methods.- Finite Volume Methods.- Solution of Linear Equation Systems.- Methods for Unsteady Problems.- Solution of the Navier-Stokes Equations.- Complex Geometries.- Turbulent Flows.- Compressible Flow.- Efficiency and Accuracy Improvement. Special Topics.- Appendeces.
7,066 citations
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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.
Abstract: SUMMARY A generalization of the coefficient of determination R2 to general regression models is discussed. A modification of an earlier definition to allow for discrete models is proposed.
5,085 citations
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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.
Abstract: background There has been an outbreak of the severe acute respiratory syndrome (SARS) worldwide. We report the clinical, laboratory, and radiologic features of 138 cases of suspected SARS during a hospital outbreak in Hong Kong. methods From March 11 to 25, 2003, all patients with suspected SARS after exposure to an index patient or ward were admitted to the isolation wards of the Prince of Wales Hospital. Their demographic, clinical, laboratory, and radiologic characteristics were analyzed. Clinical end points included the need for intensive care and death. Univariate and multivariate analyses were performed. results There were 66 male patients and 72 female patients in this cohort, 69 of whom were health care workers. The most common symptoms included fever (in 100 percent of the patients); chills, rigors, or both (73.2 percent); and myalgia (60.9 percent). Cough and headache were also reported in more than 50 percent of the patients. Other common findings were lymphopenia (in 69.6 percent), thrombocytopenia (44.8 percent), and elevated lactate dehydrogenase and creatine kinase levels (71.0 percent and 32.1 percent, respectively). Peripheral air-space consolidation was commonly observed on thoracic computed tomographic scanning. A total of 32 patients (23.2 percent) were admitted to the intensive care unit; 5 patients died, all of whom had coexisting conditions. In a multivariate analysis, the independent predictors of an adverse outcome were advanced age (odds ratio per decade of life, 1.80; 95 percent confidence interval, 1.16 to 2.81; P=0.009), a high peak lactate dehydrogenase level (odds ratio per 100 U per liter, 2.09; 95 percent confidence interval, 1.28 to 3.42; P=0.003), and an absolute neutrophil count that exceeded the upper limit of the normal range on presentation (odds ratio, 1.60; 95 percent confidence interval, 1.03 to 2.50; P=0.04). conclusions SARS is a serious respiratory illness that led to significant morbidity and mortality in our cohort.
2,256 citations
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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.
2,194 citations
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01 Oct 1991
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.
Abstract: INTRODUCTION Some Examples The Scope of this Book Use of Statistical Software STATISTICAL INFERENCE FOR BINARY DATA The Binomial Distribution Inference about the Success Probability Comparison of Two Proportions Comparison of Two or More Proportions MODELS FOR BINARY AND BINOMIAL DATA Statistical Modelling Linear Models Methods of Estimation Fitting Linear Models to Binomial Data Models for Binomial Response Data The Linear Logistic Model Fitting the Linear Logistic Model to Binomial Data Goodness of Fit of a Linear Logistic Model Comparing Linear Logistic Models Linear Trend in Proportions Comparing Stimulus-Response Relationships Non-Convergence and Overfitting Some other Goodness of Fit Statistics Strategy for Model Selection Predicting a Binary Response Probability BIOASSAY AND SOME OTHER APPLICATIONS The Tolerance Distribution Estimating an Effective Dose Relative Potency Natural Response Non-Linear Logistic Regression Models Applications of the Complementary Log-Log Model MODEL CHECKING Definition of Residuals Checking the Form of the Linear Predictor Checking the Adequacy of the Link Function Identification of Outlying Observations Identification of Influential Observations Checking the Assumption of a Binomial Distribution Model Checking for Binary Data Summary and Recommendations OVERDISPERSION Potential Causes of Overdispersion Modelling Variability in Response Probabilities Modelling Correlation Between Binary Responses Modelling Overdispersed Data A Model with a Constant Scale Parameter The Beta-Binomial Model Discussion MODELLING DATA FROM EPIDEMIOLOGICAL STUDIES Basic Designs for Aetiological Studies Measures of Association Between Disease and Exposure Confounding and Interaction The Linear Logistic Model for Data from Cohort Studies Interpreting the Parameters in a Linear Logistic Model The Linear Logistic Model for Data from Case-Control Studies Matched Case-Control Studies MIXED MODELS FOR BINARY DATA Fixed and Random Effects Mixed Models for Binary Data Multilevel Modelling Mixed Models for Longitudinal Data Analysis Mixed Models in Meta-Analysis Modelling Overdispersion Using Mixed Models EXACT METHODS Comparison of Two Proportions Using an Exact Test Exact Logistic Regression for a Single Parameter Exact Hypothesis Tests Exact Confidence Limits for bk Exact Logistic Regression for a Set of Parameters Some Examples Discussion SOME ADDITIONAL TOPICS Ordered Categorical Data Analysis of Proportions and Percentages Analysis of Rates Analysis of Binary Time Series Modelling Errors in the Measurement of Explanatory Variables Multivariate Binary Data Analysis of Binary Data from Cross-Over Trials Experimental Design COMPUTER SOFTWARE FOR MODELLING BINARY DATA Statistical Packages for Modelling Binary Data Interpretation of Computer Output Using Packages to Perform Some Non-Standard Analyses Appendix A: Values of logit(p) and probit(p) Appendix B: Some Derivations Appendix C: Additional Data Sets References Index of Examples Index
1,573 citations