Diagnostic Performance of an Antigen Test with RT-PCR for the Detection of SARS-CoV-2 in a Hospital Setting — Los Angeles County, California, June–August 2020

Brihn, Auguste; Chang, Jamie; OYong, Kelsey; Balter, Sharon; Terashita, Dawn; Rubin, Zach; Yeganeh, Nava

doi:10.15585/mmwr.mm7019a3

Cited by 71 publications

(62 citation statements)

References 5 publications

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“…Additionally, we recognize that the outcome in this study is a positive test which is not definitive proof of infection. Given that the majority of tests were PCR tests, which have high sensitivity and specificity if performed correctly, we believe this to be a sufficient stand-in for SARS-CoV-2 infection [ 35 ]. Another limitation is that the most recent data for air pollution was collected in 2010.…”

Section: Discussionmentioning

confidence: 99%

The Social and Natural Environment’s Impact on SARS-CoV-2 Infections in the UK Biobank

Scalsky

Chen

Zhao

et al. 2022

IJERPH

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COVID-19 has caused a global pandemic with considerable impact. Studies have examined the influence of socioeconomic status and air pollution on COVID-19 risk but in low detail. This study seeks to further elucidate the nuances of socioeconomic status, as defined by the Index of Multiple Deprivation (IMD), air pollution, and their relationship. We examined the effect of IMD and air pollution on the likelihood of testing positive for SARS-CoV-2 among 66,732 UKB participants tested for SARS-CoV-2 from 16 March 2020 through 16 March 2021. Logistic regression was performed controlling for age, sex, ancestry and IMD or air pollution in the respective models. IMD and its sub-scores were significantly associated with increased risk of testing positive for SARS-CoV-2. All particulate matter less than 2.5 μm (PM2.5), nitrogen oxide (NOx), and nitrogen dioxide (NO2) levels were associated with increased likelihood of testing positive for SARS-CoV-2. Measures of green space and natural environment around participants’ homes were associated with reduced likelihood of SARS-CoV-2. Socioeconomic status and air pollution have independent effects on the risk of testing positive for SARS-CoV-2. Green space and natural environment space in the proximity of people’s homes may mediate the effect of air pollution on the risk of testing positive for SARS-CoV-2.

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Section: Discussionmentioning

confidence: 99%

The Social and Natural Environment’s Impact on SARS-CoV-2 Infections in the UK Biobank

Scalsky

Chen

Zhao

et al. 2022

IJERPH

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“…RT-PCR is an excellent tool for more precisely identifying normal categories. Additionally, the approach reliably identifies COVID-positive people, with classification accuracy ranging from 65 to 95 percent [30]. While the standard RTPCR-based approach accurately predicts normal categories, COVID patients' risk is unnoticed.…”

Section: Literature Surveymentioning

confidence: 99%

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Biswas¹,

Sen

Bhatia

et al. 2021

ijsmr

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Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem.

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“…Many challenges, however, still remain in the COVID-19 detection research. The RT-PCR sensitivity rate is 60–70%, and the test is expensive [ 7 ]. Antigen detection tests, or commonly known as rapid COVID tests, are fairly accurate in detecting positive cases but the false negative rate of the test is high and the sensitivity of the test is low (60.5%) [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

Chakraborty

Murali

Mitra

2022

IJERPH

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The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19.

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Diagnostic Performance of an Antigen Test with RT-PCR for the Detection of SARS-CoV-2 in a Hospital Setting — Los Angeles County, California, June–August 2020

Cited by 71 publications

References 5 publications

The Social and Natural Environment’s Impact on SARS-CoV-2 Infections in the UK Biobank

The Social and Natural Environment’s Impact on SARS-CoV-2 Infections in the UK Biobank

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images

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