Assessment of SARS-CoV-2 viral loads in combined nasal-and-throat swabs collected from COVID-19 individuals under the Universal Community Testing Programme in Hong Kong

Mak, Gannon C.K.; Ng, Anita Y.Y.; Lam, Edmund Wing Wo; Chan, RW; Tsang, Dnc

doi:10.1016/j.jviromet.2021.114396

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…There are three popular diagnosis approaches. The first is viral tests (Mak et al 2022 ), generally via a nasopharyngeal swab (Savela et al 2022 ), which involves analyzing samples to gauge the existing presence of SARS-CoV-2. The viral test includes nucleic acid amplification test (NAAT) (Stadelman et al 2022 ) and antigen test (Urrutikoetxea-Gutierrez et al 2023 ).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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“…In the present information, the PCR is the most effective in diagnosing COVID-19 [13] and is carried out by swabbing the oral or nasal area (Fig 1.a and 1.b) [10]. This method is strongly influenced by how those are carried out, thus affecting the results of the PCR examination and eventually can lead to false negatives/positives.…”

Section: A Swab On the Robot Development Frameworkmentioning

confidence: 79%

“…Information Technology (IT), the Internet of Things (IoT), Artificial intelligence (AI), as well as wireless aspects of smart technology are some of those that have been widely utilized in medical equipment to support paramedics in to fight against COVID-19 [7], [8]. Real-time polymerase chain reaction (RT-PCR) is a widely-used tool for COVID-19 detection [9], [10]. However, the risk of medical personnel being infected by aerosols from patients is quite high since there is still direct contact during the sampling process [11].…”

Section: Introductionmentioning

confidence: 99%

Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures

Astawa

Subagia

Vista

et al. 2023

JOIV : Int. J. Inform. Visualization

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The SARS-CoV-2 virus has been the precursor of the coronavirus disease (COVID-19). The symptoms of COVID-19 begin with the common cold and then become very severe, such as those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Currently, polymerase chain reaction (PCR) is used to detect COVID-19 accurately, but it causes some side effects to the patient when the test is performed. Therefore, the proposed "Roboswab" was developed that uses thermal imaging to measure non-contact facial and oral temperature. This study focuses on the performance of the proposed equipment in measuring facial and oral temperature from various distances. Face detection also involves checking whether the subject is wearing a mask or not. Image processing methods with thermal imaging and robotic manipulators are integrated into a contact-free detector that is inexpensive, accurate, and painless. This research has successfully detected masked or non-masked faces and accurately detected facial temperature. The results showed that the accurate measurement of facial temperature with a mask is 90% with an error of +/- 0.05%, while it was 100% without a mask. On the other hand, the oral temperature was measured with 97% accuracy and an error of less than 5%. The optimal distance of the Roboswab to the face for measuring temperature is an average of 60 cm. The Roboswab tool equipped with masked or non-masked face detection can be used for early detection of COVID-19 without direct contact with patients.

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“…According to one assessment of SARS-CoV-2 viral load in the Universal Community Testing Programme of Hong Kong, the range of C t values fell between 16.28–36.94, and the mean C t values of the asymptomatic individuals and symptomatic individuals were 30.43 and 26.65, respectively. 24 Moreover, according to the model-driven predictions, 100 copies per μL sensitivity is sufficient for effective large-scale screening. 25,26 Based on these studies, the FLIPNAS sensitivity fulfilled the requirement for the mass screening of asymptomatic infections in the community.…”

Section: Resultsmentioning

confidence: 99%

“…The clinical assessment explicated that the range of C t values of asymptomatic cases is 24.26–35.96 in Hong Kong. 24 Thus, the detection range of our method can cover the local viral loads when 2 positive subsamples are present in a pool.…”

Section: Resultsmentioning

confidence: 99%