Browse
Submit Data
Databases
API
Help

Dataset Information

26 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transmission of SARS-CoV-2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event.

ABSTRACT: During the 2020 COVID-19 pandemic, an outbreak occurred following attendance of a symptomatic index case at a weekly rehearsal on 10 March of the Skagit Valley Chorale (SVC). After that rehearsal, 53 members of the SVC among 61 in attendance were confirmed or strongly suspected to have contracted COVID-19 and two died. Transmission by the aerosol route is likely; it appears unlikely that either fomite or ballistic droplet transmission could explain a substantial fraction of the cases. It is vital to identify features of cases such as this to better understand the factors that promote superspreading events. Based on a conditional assumption that transmission during this outbreak was dominated by inhalation of respiratory aerosol generated by one index case, we use the available evidence to infer the emission rate of aerosol infectious quanta. We explore how the risk of infection would vary with several influential factors: ventilation rate, duration of event, and deposition onto surfaces. The results indicate a best-estimate emission rate of 970 ± 390 quanta/h. Infection risk would be reduced by a factor of two by increasing the aerosol loss rate to 5 h^-1 and shortening the event duration from 2.5 to 1 h.

SUBMITTER: Miller SL

PROVIDER: S-EPMC7537089 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Aerosol Transmission of SARS-CoV-2: Inhalation as well as Exhalation Matters for COVID-19.

Project description: Not available

| S-EPMC8048753 | biostudies-literature

A hospital cluster of COVID-19 associated with a SARS-CoV-2 superspreading event.

Project description:Background/purposeSuperspreading events (SSEs) are pivotal in the spread of SARS-CoV-2. This study aimed to investigate an SSE of COVID-19 in a hospital and explore the transmission dynamics and heterogeneity of SSE.MethodsWe performed contact tracing for all close contacts in a cluster. We did nasopharyngeal or throat swabbing for SARS-CoV-2 by real-time RT-PCR. Environmental survey was performed. The epidemiological and clinical characteristics of the SSE were studied.ResultsPatient 1 with congestive heart failure and cellulitis, who had onset of COVID-19 two weeks after hospitalization, was the index case. Patient 1 led to 8 confirmed cases, including four health care workers (HCW). Persons tested positive for SARS-CoV-2 were HCW (n = 4), patient 1's family (n = 2), an accompanying person of an un-infected in-patient (n = 1), and an in-patient admitted before the SSE (n = 1). The attack rate among the HCW was 3.2 % (4/127). Environmental survey confirmed contamination at the bed rails, mattresses, and sink in the room patient 1 stayed, suggesting fomite transmission. The index case's sputum remained positive on illness day 35. Except one asymptomatic patient, at least three patients acquired the infection from the index case at the pre-symptomatic period. The effective reproduction number (Rt) was 0.9 (8/9).ConclusionThe host factor (heart failure, longer viral shedding), transmissibility of SARS-CoV-2 (Rt, pre-symptomatic transmission), and possible multiple modes of transmission altogether contributed to the SSE. Rapid response and advance deployment of multi-level protection in hospitals could mitigate COVID-19 transmission to one generation, thereby reducing its impact on the healthcare system.

| S-EPMC8294754 | biostudies-literature

Superspreading Event of SARS-CoV-2 Infection at a Bar, Ho Chi Minh City, Vietnam.

Project description:We report a superspreading event of severe acute respiratory syndrome coronavirus 2 infection initiated at a bar in Vietnam with evidence of symptomatic and asymptomatic transmission, based on ministry of health reports, patient interviews, and whole-genome sequence analysis. Crowds in enclosed indoor settings with poor ventilation may be considered at high risk for transmission.

| S-EPMC7774544 | biostudies-literature

Aerosol Dynamics Model for Estimating the Risk from Short-Range Airborne Transmission and Inhalation of Expiratory Droplets of SARS-CoV-2.

Project description:The highly infectious SARS-CoV-2 novel coronavirus has resulted in a global pandemic. More than a hundred million people are already impacted, with infected numbers expected to go up. Coughing, sneezing, and even talking emit respiratory droplets which can carry infectious viruses. It is important to understand how the exhaled particles move through air to an exposed person to better predict the airborne transmission impacts of SARS-CoV-2. There are many studies conducted on the airborne spread of viruses causing diseases such as SARS and measles; however, there are very limited studies that couple the transport characteristics with the aerosol dynamics of the droplets. In this study, a comprehensive model for simultaneous droplet evaporation and transport due to diffusion, convection, and gravitational settling is developed to determine the near spatial and temporal concentration of the viable virus exhaled by the infected individual. The exposure to the viable virus is estimated by calculating the respiratory deposition, and the risk of infection is determined using a dose-response model. The developed model is used to quantify the risk of short-range airborne transmission of SARS-CoV-2 from inhalation of virus-laden droplets when an infected individual is directly in front of the person exposed and the surrounding air is stagnant. The effect of different parameters, such as viral load, infectivity factor, emission sources, physical separation, exposure time, ambient air velocity, dilution, and mask usage, is determined on the risk of exposure.

| S-EPMC8231662 | biostudies-literature

Reply to Philip <i>et al.</i>: Aerosol Transmission of SARS-CoV-2: Inhalation as well as Exhalation Matters for COVID-19.

Project description: Not available

| S-EPMC8048754 | biostudies-literature

Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA.

Project description:It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and superspreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to integrate individual surveillance data with geolocation data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the state of Georgia. First, our results show that the reproductive number reduced to below one in about 2 wk after the shelter-in-place order. Superspreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance toward later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected nonelderly cases (<60 y) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of superspreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of superspreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

| S-EPMC7486752 | biostudies-literature

Superspreading events in the transmission dynamics of SARS-CoV-2: Opportunities for interventions and control.

Project description:Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the etiological agent of the Coronavirus Disease 2019 (COVID-19) disease, has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used-appropriately and less appropriately-to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, often dominated by a small number of individuals, and heavily influenced by superspreading events (SSEs). The distinct transmission features of SARS-CoV-2, e.g., high stochasticity under low prevalence (as compared to other pathogens, such as influenza), and the central role played by SSEs on transmission dynamics cannot be overlooked. Many explosive SSEs have occurred in indoor settings, stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, produce processing facilities, fish factories, cruise ships, family gatherings, parties, and nightclubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity to effectively contain outbreaks with targeted interventions to eliminate SSEs. Here, we describe the different types of SSEs, how they influence transmission, empirical evidence for their role in the COVID-19 pandemic, and give recommendations for control of SARS-CoV-2.

| S-EPMC7685463 | biostudies-literature

SARS-CoV-2 spillover transmission due to recombination event.

Project description:In late 2019, a novel Coronavirus emerged in China. Perceiving the modulating factors of cross-species virus transmission is critical to elucidate the nature of virus emergence. Using bioinformatics tools, we analyzed the mapping of the SARS-CoV-2 genome, modeling of protein structure, and analyze the evolutionary origin of SARS-CoV-2, as well as potential recombination events. Phylogenetic tree analysis shows that SARS-CoV-2 has the closest evolutionary relationship with Bat-SL-CoV-2 (RaTG13) at the scale of the complete virus genome, and less similarity to Pangolin-CoV. However, the Receptor Binding Domain (RBD) of SARS-CoV-2 is almost identical to Pangolin-CoV at the aa level, suggesting that spillover transmission probably occurred directly from pangolins, but not bats. Further recombination analysis revealed the pathway for spillover transmission from Bat-SL-CoV-2 and Pangolin-CoV. Here, we provide evidence for recombination event between Bat-SL-CoV-2 and Pangolin-CoV that resulted in the emergence of SARS-CoV-2. Nevertheless, the role of mutations should be noted as another influencing factor in the continuing evolution and resurgence of novel SARS-CoV-2 variants.

| S-EPMC7884226 | biostudies-literature

Epidemiology of SARS-CoV-2 transmission and superspreading in Salt Lake County, Utah, March-May 2020.

Project description:BackgroundUnderstanding the drivers of SARS-CoV-2 transmission can inform the development of interventions. We evaluated transmission identified by contact tracing investigations between March-May 2020 in Salt Lake County, Utah, to quantify the impact of this intervention and identify risk factors for transmission.MethodsRT-PCR positive and untested symptomatic contacts were classified as confirmed and probable secondary case-patients, respectively. We compared the number of case-patients and close contacts generated by different groups, and used logistic regression to evaluate factors associated with transmission.ResultsData were collected on 184 index case-patients and up to six generations of contacts. Of 1,499 close contacts, 374 (25%) were classified as secondary case-patients. Decreased transmission odds were observed for contacts aged <18 years (OR = 0.55 [95% CI: 0.38-0.79]), versus 18-44 years, and for workplace (OR = 0.36 [95% CI: 0.23-0.55]) and social (OR = 0.44 [95% CI: 0.28-0.66]) contacts, versus household contacts. Higher transmission odds were observed for case-patient's spouses than other household contacts (OR = 2.25 [95% CI: 1.52-3.35]). Compared to index case-patients identified in the community, secondary case-patients identified through contract-tracing generated significantly fewer close contacts and secondary case-patients of their own. Transmission was heterogeneous, with 41% of index case-patients generating 81% of directly-linked secondary case-patients.ConclusionsGiven sufficient resources and complementary public health measures, contact tracing can contain known chains of SARS-CoV-2 transmission. Transmission is associated with age and exposure setting, and can be highly variable, with a few infections generating a disproportionately high share of onward transmission.

| S-EPMC10289415 | biostudies-literature

Superspreading of SARS-CoV-2 in the USA.

Project description:A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, β, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that σβ/μβ ≳ 3.2, where μβ is the mean infectiousness and σβ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.

| S-EPMC7993775 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data