Diagnosing COVID-19: The Disease and Tools for Detection
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Machine scores (provisional)
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- Teacher spread
- 0.241 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
COVID-19 has spread globally since its discovery in Hubei province, China in December 2019. A combination of computed tomography imaging, whole genome sequencing, and electron microscopy were initially used to screen and identify SARS-CoV-2, the viral etiology of COVID-19. The aim of this review article is to inform the audience of diagnostic and surveillance technologies for SARS-CoV-2 and their performance characteristics. We describe point-of-care diagnostics that are on the horizon and encourage academics to advance their technologies beyond conception. Developing plug-and-play diagnostics to manage the SARS-CoV-2 outbreak would be useful in preventing future epidemics.
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The record
- Venue
- ACS Nano
- Topic
- SARS-CoV-2 detection and testing
- Field
- Medicine
- Canadian institutions
- Hospital for Sick ChildrenSunnybrook Health Science CentrePublic Health OntarioUniversity of Toronto
- Funders
- Natural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
- Keywords
- Coronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Outbreak2019-20 coronavirus outbreakPoint of carePoint-of-care testingData scienceVirologyPandemicDiseaseComputer scienceComputational biologyMedicineBiologyInfectious disease (medical specialty)Pathology
- Has abstract in OpenAlex
- yes