COVID-19 Rapid Antigen Test as Screening Strategy at Points of Entry: Experience in Lazio Region, Central Italy, August–October 2020

Colavita, Francesca; Vairo, Francesco; Meschi, Silvia; Valli, Maria Beatrice; Lalle, Eleonora; Castilletti, Concetta; Fusco, Danilo; Spiga, Giuseppe; Bartoletti, Pierluigi; Ursino, Simona; Sanguinetti, Maurizio; Di, Antonino; Vaia, Francesco; Ippolito, Giuseppe; Capobianchi, Maria Rosaria

doi:10.3390/biom11030425

Cited by 28 publications

(22 citation statements)

References 13 publications

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“…However, it is expensive, has a relative long turn-around-time, and require special laboratory set-up with fixed laboratory capacity. The need for larger test capacity and rapid case identification lead to the adoption of a point-of-care (POC) rapid antigen test in some screening programs ( 4 ). The test is cheap, rapid, easy to use, and does not require a laboratory setting ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

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The Performance of Two Rapid Antigen Tests During Population-Level Screening for SARS-CoV-2 Infection

et al. 2021

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Background: SARS-CoV-2 antigen assays offer a rapid mean to diagnose and isolate infected individuals. However, their utility in population-level screening is unknown.Objectives: The performance of two antigen tests in detecting SARS-CoV-2 was assessed among individuals randomly selected in the community.Study Design: A prospective study that performed head-to-head comparison of two SARS-CoV-2 antigen assays. Individuals were recruited during community SARS-CoV-2 screening over 10 working days. Demographic and clinical data were collected. Standard Q COVID-19 Ag test, a point-of-care chromatographic assay, was conducted immediately, and then the sample was transported to the virology laboratory to perform PCR and the LIAISON SARS-CoV-2 Ag chemiluminesence immunoassay.Results: respiratory samples from 991 individuals were collected, and 62 were positive by PCR. Inconclusive PCR results were observed in 19 samples and were excluded. The median age of participants was 40.2 years (IQR 32.3–47.8), and 932 (94%) were males. Most (77.4%) of infections were asymptomatic. The sensitivity and the specificity of the LIAISON assay were 43.3% (95%CI 30.6–56.8) and 99.9% (95%CI 99.3–100). The Standard Q assay had lower sensitivity (30.6%, 95%CI 19.6–43.7) but similar specificity (98.8%, 95%CI, 97.8–99.4). Similarly, the LIAISON assay had higher positive predictive value (96.3%, 95%CI 81–99.9% vs. 63.3%, 95%CI, 43.9–80.1%). Both assays performed better in symptomatic patients and among samples with a low-cycle threshold (Ct < 25).Conclusion: In our setting of random community surveillance, rapid antigen testing of nasopharyngeal swabs by either LIAISON SARS-CoV-2 Ag (DiaSorin) or Standard Q COVID-19 Ag (SD Biosensor) was less sensitive to detecting SARS-CoV-2 than the TaqPath COVID-19 RT-PCR.

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Section: Introductionmentioning

confidence: 99%

“…These factors allow the rapid antigen testing to overcome several logistical hurdles with mass SARS-CoV-2 testing. However, variation in test performance when used in different settings has been raised ( 3 , 4 , 6 , 7 ).…”

Section: Introductionmentioning

confidence: 99%

The Performance of Two Rapid Antigen Tests During Population-Level Screening for SARS-CoV-2 Infection

et al. 2021

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“…It is, therefore, of utmost importance to screen the positive COVID-19 patients accurately for efficient utilization of limited resources. Two types of viral tests are currently popularly used to detect COVID-19 infection: Nucleic Acid Amplification Tests (NAATs) [2] and Antigen Tests [3]. NAATs can reliably detect SARS-CoV-2 and are unlikely to return a false-negative result of All the authors are with Indian Institute of Technology Delhi, New Delhi 110016, India.…”

Section: A Backgroundmentioning

confidence: 99%

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Mondal¹,

Bhattacharjee²,

Singla³

et al. 2021

Preprint

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Since its outbreak, the rapid growth of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area.

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“…It is, therefore, of utmost importance to screen the positive COVID-19 patients accurately for efficient utilization of limited resources. Two types of viral tests are currently popularly used to detect COVID-19 infection: Nucleic Acid Amplification Tests (NAATs) [2] and Antigen Tests [3] . NAATs can reliably detect SARS-CoV-2 and are unlikely to return a false-negative result of SARS-CoV-2.…”

Section: Introductionmentioning

confidence: 99%

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

Mondal

Bhattacharjee

Singla

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. Methods: Motivated by this, in this work, we propose the use of vision transformers (instead of convolutional networks) for COVID-19 screening using the X-ray and CT images. We employ a multi-stage transfer learning technique to address the issue of data scarcity. Furthermore, we show that the features learned by our transformer networks are explainable. Results: We demonstrate that our method not only quantitatively outperforms the recent benchmarks but also focuses on meaningful regions in the images for detection (as confirmed by Radiologists), aiding not only in accurate diagnosis of COVID-19 but also in localization of the infected area. The code for our implementation can be found here - . Conclusion: The proposed method will help in timely identification of COVID-19 and efficient utilization of limited resources.

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COVID-19 Rapid Antigen Test as Screening Strategy at Points of Entry: Experience in Lazio Region, Central Italy, August–October 2020

Cited by 28 publications

References 13 publications

The Performance of Two Rapid Antigen Tests During Population-Level Screening for SARS-CoV-2 Infection

The Performance of Two Rapid Antigen Tests During Population-Level Screening for SARS-CoV-2 Infection

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography

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