SARS-CoV-2 transmission through air

Thaper, Ravinder

doi:10.1016/j.cmrp.2020.07.005

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…This is a highly infectious disease and spreads primarily via small droplets from coughing, sneezing, and talking. The droplets are usually not airborne, however those standing in close proximity have a high probability of getting infected [ 1 , 2 ]. COVID-19 is a highly contagious disease which spreads from person to person within close vicinity.…”

Section: Introductionmentioning

confidence: 99%

A novel data augmentation approach for mask detection using deep transfer learning

Prusty

Tripathi

Dubey

2021

Intelligence-Based Medicine

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At the onset of 2020, the world saw the rise and spread of a global pandemic named COVID-19 which caused numerous deaths and affected millions of people around the world. Due to its highly contagious nature, this disease spread across the world within a short span of time. It forced almost all the nations to implement strict social distancing rules along with use of face masks to reduce the risk of getting infected. While the virus is still on loose, markets and business firms have reopened to keep the economy alive. This calls for modification of existing technological models to cater for the safety of individuals and stop the spread of virus in public places. One such stringent implementation to achieve this safety would be deployment of a mask detection model. The proposed mask detection models can serve as a vital utility in the coming years for ensuring proper enforcement of safety protocols. This research paper explores the use of state of the art YOLOv3 model, a deep transfer learning object detection technique, to develop a mask detection model. Along with the implementation of a standard approach of any object detection algorithm, this paper has proposed the use of a data augmentation approach for mask detection. The proposed model focuses on generating an augmented dataset from the standard dataset with the help of data augmentation done by using image filtering techniques such as grayscale and Gaussian blur. This augmented dataset is used for training the object detection model for mask detection. The mean average precision for the Data augmentation based mask detection model is observed to be 99.8% while training. Finally, a comparison on the model performance is evaluated for the standard and proposed augmented data approach. The experiment conducted showed that the average confidence level for Standard mask detection model was 0.94, 0.93, 0.91 for images of individuals (type A), images with groups of people (type B) and video with the group of people (type C) respectively. The average confidence levels for the Data augmentation based mask detection model for types A, B and C are 0.97, 0.96 0.93 respectively. This paper therefore concludes that the proposed Data augmentation based mask detection model performs better than the Standard mask detection model.

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Section: Introductionmentioning

confidence: 99%

A novel data augmentation approach for mask detection using deep transfer learning

Prusty

Tripathi

Dubey

2021

Intelligence-Based Medicine

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“…Infection may occur due to contact with a contaminated surface and subsequently touching one’s face, eyes, or mouth [ 4 ]. The major symptoms of illness caused by SARS-CoV-2 are sore throat, cough, fever, chills, difficulty breathing or shortness of breath, repeated shaking with chills, muscle pain, and loss of taste or smell [ 5 , 6 ]. On January 20, 2020, the first case of SARS-CoV-2 in the United States was confirmed in Snohomish County, in the state of Washington from a 35-year-old man who traveled to Wuhan, China [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Decontamination of respirators amid shortages due to SARS-CoV-2

Thaper

Fagen

2021

Photochem Photobiol Sci

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The pandemic created by SARS-CoV-2 has caused a shortage in the supplies of N95 filtering facepiece respirators (FFRs), disposable respirators with at least 95% efficiency to remove non-oily airborne particles, due to increasing cases all over the world. The current article reviewed various possible decontamination methods for FFR reuse including ultraviolet germicidal irradiation (UVGI), hydrogen peroxide vapor (HPV), microwave-generated steam (MGS), hydrogen peroxide gas plasma (HPGP), and 70% or higher ethanol solution. HPV decontamination was effective against bacterial spores (6 log 10 reduction of Geobacillus stearothermophilus spores ) on FFRs and viruses (> 4 log 10 reduction of various types of viruses) on inanimate surfaces, and no degradation of respirator materials and fit has been reported. 70% or higher ethanol decontamination showed high efficacy in inactivation of coronaviruses on inanimate surfaces (> 3.9 log 10 reduction) but it was lower on FFRs which filtration efficiency was also decreased. UVGI method had good biocidal efficacy on FFRs (> 3 log 10 reduction of H1N1 virus) combined with inexpensive, readily available equipment; however, it was more time-consuming to ensure sufficient reduction in SARS-CoV-2. MGS treatment also provided good viral decontamination on FFRs (> 4 log 10 reduction of H1N1 virus) along with less time-intensive process and readily available equipment while inconsistent disinfection on the treated surfaces and deterioration of nose cushion of FFRs were observed. HPGP was a good virucidal system (> 6 log 10 reduction of Vesicular stomatitis virus ) but filtration efficiency after decontamination was inconsistent. Overall, HPV appeared to be one of the most promising methods based on the high biocidal efficacy on FFRs, preservation of respirator performance after multiple cycles, and no residual chemical toxicity. Nonetheless, equipment cost and time of the HPV process and a suitable operating room need to be considered.

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“…The coronavirus disease 2019 (COVID-19) pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has killed over one million people as of October 1, 2020 [1] , [2] . This virus is very contagious and can spread through the air, by close contact, in aerosol droplets, by the fecal-oral route, and by contact with contaminated surfaces [1] , [3] .…”

Section: Introductionmentioning

confidence: 99%

A multiple therapy hypothesis for treatment of COVID-19 patients

Oscar

2020

Medical Hypotheses

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The coronavirus disease 2019 (COVID-19) pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has killed more than one million people as of October 1, 2020. Consequently, a search is on for a treatment that can bring the pandemic to an end. However, treatments (vaccine, antiviral, plasma) that are directed at specific viral proteins (RNA polymerase, spike proteins) may not work well against all strains of the virus. Therefore, it is hypothesized that a therapy based on multiple treatments is needed for COVID-19 patients and to bring the pandemic to an end. Here, it is proposed that a combination of cool air therapy (CAT) and purified air technology (PAT) in an oxygen species cool air respirator (OSCAR) could be used to reduce viral (SARS-CoV-2) load and severity of illness in COVID-19 patients through the individual dose–response relationship. In addition, the proposed therapy (CAT + PAT in OSCAR), which works by a more general physical and chemical mechanism, should work well with other treatments (vaccine, antiviral, plasma) that target specific viral proteins (RNA polymerase, spike proteins) to provide a safe and effective multiple therapy approach for ending the COVID-19 pandemic caused by SARS-CoV-2.

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SARS-CoV-2 transmission through air

Cited by 6 publications

References 8 publications

A novel data augmentation approach for mask detection using deep transfer learning

A novel data augmentation approach for mask detection using deep transfer learning

Decontamination of respirators amid shortages due to SARS-CoV-2

A multiple therapy hypothesis for treatment of COVID-19 patients

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