Diagnosing COVID-19: The Disease and Tools for Detection
Identifieur interne : 000036 ( Pmc/Corpus ); précédent : 000035; suivant : 000037Diagnosing COVID-19: The Disease and Tools for Detection
Auteurs : Buddhisha Udugama ; Pranav Kadhiresan ; Hannah N. Kozlowski ; Ayden Malekjahani ; Matthew Osborne ; Vanessa Y. C. Li ; Hongmin Chen ; Samira Mubareka ; Jonathan B. Gubbay ; Warren C. W. ChanSource :
- ACS Nano [ 1936-0851 ] ; 2020.
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.
Url:
DOI: 10.1021/acsnano.0c02624
PubMed: 32223179
PubMed Central: 7144809
Links to Exploration step
PMC:7144809Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Diagnosing COVID-19: The Disease and Tools for Detection</title><author><name sortKey="Udugama, Buddhisha" sort="Udugama, Buddhisha" uniqKey="Udugama B" first="Buddhisha" last="Udugama">Buddhisha Udugama</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Kadhiresan, Pranav" sort="Kadhiresan, Pranav" uniqKey="Kadhiresan P" first="Pranav" last="Kadhiresan">Pranav Kadhiresan</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Kozlowski, Hannah N" sort="Kozlowski, Hannah N" uniqKey="Kozlowski H" first="Hannah N." last="Kozlowski">Hannah N. Kozlowski</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Malekjahani, Ayden" sort="Malekjahani, Ayden" uniqKey="Malekjahani A" first="Ayden" last="Malekjahani">Ayden Malekjahani</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Osborne, Matthew" sort="Osborne, Matthew" uniqKey="Osborne M" first="Matthew" last="Osborne">Matthew Osborne</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Li, Vanessa Y C" sort="Li, Vanessa Y C" uniqKey="Li V" first="Vanessa Y. C." last="Li">Vanessa Y. C. Li</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Chen, Hongmin" sort="Chen, Hongmin" uniqKey="Chen H" first="Hongmin" last="Chen">Hongmin Chen</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Mubareka, Samira" sort="Mubareka, Samira" uniqKey="Mubareka S" first="Samira" last="Mubareka">Samira Mubareka</name><affiliation><nlm:aff id="aff3">Department of Laboratory Medicine and Pathobiology, Faculty of Medicine,<institution>University of Toronto</institution>, Toronto, Ontario M5S 1A1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4">Biological Sciences,<institution>Sunnybrook Research Institute</institution>, Toronto, Ontario M4N 3M5,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Gubbay, Jonathan B" sort="Gubbay, Jonathan B" uniqKey="Gubbay J" first="Jonathan B." last="Gubbay">Jonathan B. Gubbay</name><affiliation><nlm:aff id="aff3">Department of Laboratory Medicine and Pathobiology, Faculty of Medicine,<institution>University of Toronto</institution>, Toronto, Ontario M5S 1A1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Public Health Ontario</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><institution>Hospital for Sick Children</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Chan, Warren C W" sort="Chan, Warren C W" uniqKey="Chan W" first="Warren C. W." last="Chan">Warren C. W. Chan</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Department of Chemistry,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3H6,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff8">Materials Science and Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32223179</idno><idno type="pmc">7144809</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144809</idno><idno type="RBID">PMC:7144809</idno><idno type="doi">10.1021/acsnano.0c02624</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000036</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000036</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Diagnosing COVID-19: The Disease and Tools for Detection</title><author><name sortKey="Udugama, Buddhisha" sort="Udugama, Buddhisha" uniqKey="Udugama B" first="Buddhisha" last="Udugama">Buddhisha Udugama</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Kadhiresan, Pranav" sort="Kadhiresan, Pranav" uniqKey="Kadhiresan P" first="Pranav" last="Kadhiresan">Pranav Kadhiresan</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Kozlowski, Hannah N" sort="Kozlowski, Hannah N" uniqKey="Kozlowski H" first="Hannah N." last="Kozlowski">Hannah N. Kozlowski</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Malekjahani, Ayden" sort="Malekjahani, Ayden" uniqKey="Malekjahani A" first="Ayden" last="Malekjahani">Ayden Malekjahani</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Osborne, Matthew" sort="Osborne, Matthew" uniqKey="Osborne M" first="Matthew" last="Osborne">Matthew Osborne</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Li, Vanessa Y C" sort="Li, Vanessa Y C" uniqKey="Li V" first="Vanessa Y. C." last="Li">Vanessa Y. C. Li</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Chen, Hongmin" sort="Chen, Hongmin" uniqKey="Chen H" first="Hongmin" last="Chen">Hongmin Chen</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Mubareka, Samira" sort="Mubareka, Samira" uniqKey="Mubareka S" first="Samira" last="Mubareka">Samira Mubareka</name><affiliation><nlm:aff id="aff3">Department of Laboratory Medicine and Pathobiology, Faculty of Medicine,<institution>University of Toronto</institution>, Toronto, Ontario M5S 1A1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4">Biological Sciences,<institution>Sunnybrook Research Institute</institution>, Toronto, Ontario M4N 3M5,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Gubbay, Jonathan B" sort="Gubbay, Jonathan B" uniqKey="Gubbay J" first="Jonathan B." last="Gubbay">Jonathan B. Gubbay</name><affiliation><nlm:aff id="aff3">Department of Laboratory Medicine and Pathobiology, Faculty of Medicine,<institution>University of Toronto</institution>, Toronto, Ontario M5S 1A1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff5"><institution>Public Health Ontario</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff6"><institution>Hospital for Sick Children</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></nlm:aff></affiliation></author><author><name sortKey="Chan, Warren C W" sort="Chan, Warren C W" uniqKey="Chan W" first="Warren C. W." last="Chan">Warren C. W. Chan</name><affiliation><nlm:aff id="aff1">Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2">Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Department of Chemistry,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3H6,<country>Canada</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff8">Materials Science and Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></nlm:aff></affiliation></author></analytic><series><title level="j">ACS Nano</title><idno type="ISSN">1936-0851</idno><idno type="eISSN">1936-086X</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p content-type="toc-graphic"><graphic xlink:href="nn0c02624_0006" id="ab-tgr1"></graphic></p><p>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.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zhou, P" uniqKey="Zhou P">P. Zhou</name></author><author><name sortKey="Yang, X L" uniqKey="Yang X">X.-L. Yang</name></author><author><name sortKey="Wang, X G" uniqKey="Wang X">X.-G. Wang</name></author><author><name sortKey="Hu, B" uniqKey="Hu B">B. Hu</name></author><author><name sortKey="Zhang, L" uniqKey="Zhang L">L. Zhang</name></author><author><name sortKey="Zhang, W" uniqKey="Zhang W">W. Zhang</name></author><author><name sortKey="Si, H R" uniqKey="Si H">H.-R. Si</name></author><author><name sortKey="Zhu, Y" uniqKey="Zhu Y">Y. Zhu</name></author><author><name sortKey="Li, B" uniqKey="Li B">B. Li</name></author><author><name sortKey="Huang, C L" uniqKey="Huang C">C.-L. Huang</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Ai, T" uniqKey="Ai T">T. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">ACS Nano</journal-id><journal-id journal-id-type="iso-abbrev">ACS Nano</journal-id><journal-id journal-id-type="publisher-id">nn</journal-id><journal-id journal-id-type="coden">ancac3</journal-id><journal-title-group><journal-title>ACS Nano</journal-title></journal-title-group><issn pub-type="ppub">1936-0851</issn><issn pub-type="epub">1936-086X</issn><publisher><publisher-name>American Chemical Society</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32223179</article-id><article-id pub-id-type="pmc">7144809</article-id><article-id pub-id-type="doi">10.1021/acsnano.0c02624</article-id><article-categories><subj-group><subject>Review</subject></subj-group></article-categories><title-group><article-title>Diagnosing COVID-19: The Disease and Tools for Detection</article-title></title-group><contrib-group><contrib contrib-type="author" id="ath1"><name><surname>Udugama</surname><given-names>Buddhisha</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath2"><name><surname>Kadhiresan</surname><given-names>Pranav</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath3"><name><surname>Kozlowski</surname><given-names>Hannah N.</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath4"><name><surname>Malekjahani</surname><given-names>Ayden</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath5"><name><surname>Osborne</surname><given-names>Matthew</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath6"><name><surname>Li</surname><given-names>Vanessa Y. C.</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath7"><name><surname>Chen</surname><given-names>Hongmin</given-names></name><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="notes1" ref-type="notes">◆</xref></contrib><contrib contrib-type="author" id="ath8"><name><surname>Mubareka</surname><given-names>Samira</given-names></name><xref rid="aff3" ref-type="aff">§</xref><xref rid="aff4" ref-type="aff">∥</xref></contrib><contrib contrib-type="author" id="ath9"><name><surname>Gubbay</surname><given-names>Jonathan B.</given-names></name><xref rid="aff3" ref-type="aff">§</xref><xref rid="aff5" ref-type="aff">⊥</xref><xref rid="aff6" ref-type="aff">#</xref></contrib><contrib contrib-type="author" corresp="yes" id="ath10"><name><surname>Chan</surname><given-names>Warren C. W.</given-names></name><xref rid="cor1" ref-type="other">*</xref><xref rid="aff1" ref-type="aff">†</xref><xref rid="aff2" ref-type="aff">‡</xref><xref rid="aff7" ref-type="aff">∇</xref><xref rid="aff8" ref-type="aff">○</xref></contrib><aff id="aff1"><label>†</label>Institute of Biomaterials and Biomedical Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></aff><aff id="aff2"><label>‡</label>Terrence Donnelly Center for Cellular and Biomolecular Research,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3E1,<country>Canada</country></aff><aff id="aff3"><label>§</label>Department of Laboratory Medicine and Pathobiology, Faculty of Medicine,<institution>University of Toronto</institution>, Toronto, Ontario M5S 1A1,<country>Canada</country></aff><aff id="aff4"><label>∥</label>Biological Sciences,<institution>Sunnybrook Research Institute</institution>, Toronto, Ontario M4N 3M5,<country>Canada</country></aff><aff id="aff5"><label>⊥</label><institution>Public Health Ontario</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></aff><aff id="aff6"><label>#</label><institution>Hospital for Sick Children</institution>, Toronto, Ontario M5G 1V2,<country>Canada</country></aff><aff id="aff7"><label>∇</label>Department of Chemistry,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3H6,<country>Canada</country></aff><aff id="aff8"><label>○</label>Materials Science and Engineering,<institution>University of Toronto</institution>, Toronto, Ontario M5S 3G9,<country>Canada</country></aff></contrib-group><author-notes><corresp id="cor1"><label>*</label>Email: <email>warren.chan@utoronto.ca</email>.</corresp></author-notes><pub-date pub-type="epub"><day>30</day><month>03</month><year>2020</year></pub-date><elocation-id>acsnano.0c02624</elocation-id><history><date date-type="received"><day>27</day><month>03</month><year>2020</year></date><date date-type="accepted"><day>30</day><month>03</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 American Chemical Society</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>American Chemical Society</copyright-holder><license license-type="open-access"><license-p>This article is made available via the PMC Open Access Subset for unrestricted RESEARCH
re-use and analyses in any form or by any means with acknowledgement of the original
source. These permissions are granted for the duration of the World Health Organization
(WHO) declaration of COVID-19 as a global pandemic.</license-p></license></permissions><abstract><p content-type="toc-graphic"><graphic xlink:href="nn0c02624_0006" id="ab-tgr1"></graphic></p><p>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.</p></abstract><kwd-group><kwd>SARS-CoV-2</kwd><kwd>diagnostics</kwd><kwd>COVID-19</kwd><kwd>PCR</kwd><kwd>surveillance</kwd><kwd>pandemic</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>document-id-old-9</meta-name><meta-value>nn0c02624</meta-value></custom-meta><custom-meta><meta-name>document-id-new-14</meta-name><meta-value>nn0c02624</meta-value></custom-meta><custom-meta><meta-name>ccc-price</meta-name><meta-value></meta-value></custom-meta></custom-meta-group></article-meta><notes id="notes-d1e21-autogenerated"><fn-group><fn fn-type="" id="d30e255"><p>This article is made available for a limited time sponsored by ACS under the <ext-link ext-link-type="uri" xlink:href="http://pubs.acs.org/page/policy/freetoread/index.html">ACS Free to Read
License</ext-link>, which permits copying and redistribution of the article for
non-commercial scholarly purposes.</p></fn></fn-group></notes></front><body><p id="sec1">Coronavirus disease 2019 (COVID-19) was discovered in Hubei Province, China in December
2019.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> A cluster of patients were admitted with fever, cough, shortness
of breath, and other symptoms.<sup><xref ref-type="bibr" rid="ref2">2</xref></sup> Patients were scanned using computed
tomography (CT), which revealed varied opacities (denser, more profuse, and confluent) in
comparison to images of healthy lungs.<sup><xref ref-type="bibr" rid="ref3">3</xref></sup> This finding led to the initial
diagnosis of pneumonia. Additional nucleic acid analysis using multiplex real-time
polymerase chain reaction (PCR) of known pathogen panels led to negative results, suggesting
that the cause of pneumonia was of unknown origin.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> By January 10, 2020,
samples from patients’ bronchoalveolar lavage (BAL) fluid were analyzed to reveal a
pathogen with a similar genetic sequence to the betacoronavirus B lineage. It was discovered
that this new pathogen had ∼80%, ∼50%, and ∼96% similarity to the
genome of the severe acute respiratory syndrome virus (SARS-CoV), Middle East respiratory
syndrome virus (MERS-CoV), and bat coronavirus RaTG13, respectively.<sup><xref ref-type="bibr" rid="ref1">1</xref>,<xref ref-type="bibr" rid="ref4">4</xref></sup> The novel coronavirus was named
SARS-CoV-2, the pathogen causing COVID-19. As of April 2, 2020, the disease has spread to at
least 202 countries, infected over 1 million people, and resulted in at least 45,526 deaths
globally. It is suspected that the total number of reported COVID-19 infections is
underestimated, as there are many mild or asymptomatic cases that go undetected.<sup><xref ref-type="bibr" rid="ref5">5</xref></sup> From the Diamond Princess cruise ship case study, an estimate of 17.9% of
asymptomatic cases were reported. Asymptomatic individuals are as infectious as symptomatic
individuals and are therefore capable of further spreading the disease.<sup><xref ref-type="bibr" rid="ref6">6</xref></sup></p><p>SARS-CoV-2 can be transmitted from human to human. The current hypothesis is that the first
transmission occurred between bats and a yet-to-be-determined intermediate host
animal.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> It is estimated that a SARS-CoV-2-infected person will infect
approximately three new people (the reproductive number is averaged to be 3.28).<sup><xref ref-type="bibr" rid="ref7">7</xref></sup> The symptoms can vary, with some patients remaining asymptomatic, while
others present with fever, cough, fatigue, and a host of other symptoms. The symptoms may be
similar to patients with influenza or the common cold. At this stage, the most likely mode
of transmission is thought to be through direct contact and droplet
spread.<sup><xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref9">9</xref></sup> A recent
study looking at aerosol and surface stability of SARS-CoV-2 showed that the virus may be
found in aerosols (<5 μm) for at least up to 3 h and may be more stable on plastic
and stainless steel than on copper and cardboard.<sup><xref ref-type="bibr" rid="ref10">10</xref></sup></p><p>Development of therapeutics and vaccines is underway, but there are currently no United
States Food and Drug Administration (FDA) approved therapeutics or vaccines for the
treatment of COVID-19 patients.<sup><xref ref-type="bibr" rid="ref11">11</xref>,<xref ref-type="bibr" rid="ref12">12</xref></sup> Diagnostics can play an important role in the containment of COVID-19,
enabling the rapid implementation of control measures that limit the spread through case
identification, isolation, and contact tracing (<italic>i</italic>.<italic>e</italic>.,
identifying people that may have come in contact with an infected patient). The current
diagnostic workflow for COVID-19 is described in <xref rid="fig1" ref-type="fig">Figure <xref rid="fig1" ref-type="fig">1</xref></xref>. In this review article, we aim to summarize the current known biological
properties of SARS-CoV-2, diagnostic tools and clinical results for detecting SARS-CoV-2,
emerging diagnostics, and surveillance technology to curb the spread. This is a rapidly
moving topic of research, and a review article that encompasses the current findings may be
useful for guiding strategies to deal with the current COVID-19 pandemic.</p><fig id="fig1" position="float"><label>Figure 1</label><caption><p>Example of patient and sample workflow during the COVID-19 outbreak. Patients present
at a healthcare facility for triage. The collected samples are tested on-site if
possible or transported for molecular testing and sequencing. Patients are then managed
appropriately.</p></caption><graphic xlink:href="nn0c02624_0001" id="gr1" position="float"></graphic></fig><sec id="sec1.1"><title>Biological Properties of SARS-CoV-2</title><p>SARS-CoV-2 was first identified from patient samples in Wuhan, China. Human airway
epithelial cells were cultured with the virus from BAL fluid isolated from patients.
Supernatant was collected from cells that were damaged or killed and analyzed by
negative-stained transmission electron microscopy (<xref rid="fig2" ref-type="fig">Figure <xref rid="fig2" ref-type="fig">2</xref></xref>).<sup><xref ref-type="bibr" rid="ref13">13</xref></sup> The images revealed that the virus has a
diameter ranging from 60 to 140 nm, has an envelope with protein spikes, and has genetic
material.<sup><xref ref-type="bibr" rid="ref14">14</xref></sup> The overall structure looks similar to other viruses from
the <italic>Coronaviridae</italic> family.</p><fig id="fig2" position="float"><label>Figure 2</label><caption><p>SARS-CoV-2 morphology. Transmission electron microscope image of SARS-CoV-2 spherical
viral particles in a cell.<sup><xref ref-type="bibr" rid="ref13">13</xref></sup> The virus is colorized in blue
(adapted from the US Centers for Disease Control). Representation of the viral
structure is illustrated with its structural viral proteins.</p></caption><graphic xlink:href="nn0c02624_0002" id="gr2" position="float"></graphic></fig><p>SARS-CoV-2 has a single-stranded positive sense RNA genome that is ∼30,000
nucleotides in length.<sup><xref ref-type="bibr" rid="ref1">1</xref>,<xref ref-type="bibr" rid="ref15">15</xref></sup> The genome encodes 27 proteins including an RNA-dependent RNA
polymerase (<italic>RdRP</italic>) and four structural proteins.<sup><xref ref-type="bibr" rid="ref15">15</xref>,<xref ref-type="bibr" rid="ref16">16</xref></sup><italic>RdRP</italic> acts in conjunction with nonstructural proteins to maintain genome
fidelity. A region of the <italic>RdRP</italic> gene in SARS-CoV-2 was shown to be highly
similar to a region of the <italic>RdRP</italic> gene found in bat coronavirus RaTG13 and
96% similar to the RaTG13 overall genome sequence.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> Of 104 strains
sequenced between December 2019 and mid-February 2020, 99.9% sequence homology was
observed, but, more recently, changes in the viral genome have been catalogued, showing a
higher sequence diversity.<sup><xref ref-type="bibr" rid="ref2">2</xref>,<xref ref-type="bibr" rid="ref17">17</xref></sup></p><p>The four structural proteins of SARS-CoV-2 include the spike surface glycoprotein (S),
small envelope protein (E), matrix protein (M), and nucleocapsid protein (N). In
coronaviruses, the <italic>S</italic> gene codes for the receptor-binding spike protein
that enables the virus to infect cells.<sup><xref ref-type="bibr" rid="ref18">18</xref></sup> This spike protein mediates
receptor binding and membrane fusion, which determines host tropism and transmission
capabilities.<sup><xref ref-type="bibr" rid="ref4">4</xref></sup> In SARS-CoV-2, the <italic>S</italic> gene is
divergent with <75% nucleotide sequence similarity when compared to all previously
described SARS-related coronaviruses.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> The other three structural
proteins are more conserved than the spike protein and are necessary for general
coronavirus function.<sup><xref ref-type="bibr" rid="ref15">15</xref></sup> These proteins are involved in encasing the RNA
and/or in protein assembly, budding, envelope formation, and
pathogenesis.<sup><xref ref-type="bibr" rid="ref19">19</xref>−<xref ref-type="bibr" rid="ref21">21</xref></sup></p><p>SARS-CoV-2 appears to interact with the angiotensin converting enzyme 2 (ACE2) receptor
for entry into cells. Zhou <italic>et al</italic>. conducted infectivity studies by
incubating SARS-CoV-2 with HeLa cells of differential ACE2 receptor expression.<sup><xref ref-type="bibr" rid="ref1">1</xref></sup> The authors showed co-localization of the fluorescently stained viruses
with cells that express the ACE2 receptor from Chinese horseshoe bats, pigs, humans, and
civets, but not from mice. The ACE2 mRNA is present in almost all human organs. ACE2 is
present in arterial and venous endothelial cells and arterial smooth muscle cells in the
lungs, stomach, small intestine, colon, skin, lymph nodes, liver bile ducts, kidney
parietal epithelial cells, and the brain. It is also expressed on the surface of lung
alveolar epithelial cells and enterocytes of the small intestine that allows them to be
infected. Tissues of the upper respiratory tract (<italic>i</italic>.<italic>e</italic>.,
oral and nasal mucosa and nasopharynx) did not show surface expression of ACE2 on
epithelial cells and therefore are unlikely the primary site of SARS-CoV-2
infection.<sup><xref ref-type="bibr" rid="ref22">22</xref></sup> CT scans may show higher opacity in the lower lungs
because cells in that region express more ACE2. SARS-CoV-2 has been isolated from oral
swabs, BAL fluid, and stool.<sup><xref ref-type="bibr" rid="ref1">1</xref>,<xref ref-type="bibr" rid="ref23">23</xref></sup> Higher viral loads have been recorded in the nose
<italic>versus</italic> the throat, with similar viral loads seen in asymptomatic and
symptomatic patients.<sup><xref ref-type="bibr" rid="ref24">24</xref></sup> Understanding the biological properties of
SARS-CoV-2 enabled researchers to develop diagnostics for detection.</p></sec><sec id="sec1.2"><title>Current Diagnostic Tests for COVID-19</title><p>The symptoms expressed by COVID-19 patients are nonspecific and cannot be used for an
accurate diagnosis. Guan <italic>et al</italic>. reported that 44% of 1099 COVID-19
patients from China had a fever when they entered the hospital and that 89% developed a
fever while in hospital.<sup><xref ref-type="bibr" rid="ref25">25</xref></sup> They further found that patients had a cough
(68%), fatigue (38%), sputum production (34%), and shortness of breath (19%). Many of
these symptoms could be associated with other respiratory infections. Nucleic acid testing
and CT scans have been used for diagnosing and screening COVID-19.</p><p>Molecular techniques are more suitable than syndromic testing and CT scans for accurate
diagnoses because they can target and identify specific pathogens. The development of
molecular techniques is dependent upon understanding (1) the proteomic and genomic
composition of the pathogen or (2) the induction of changes in the expression of
proteins/genes in the host during and after infection. As of March 24, 2020, the genomic
and proteomic compositions of SARS-CoV-2 have been identified, but the host response to
the virus is still under investigation. The first genome sequence of SARS-CoV-2 was
conducted with metagenomic RNA sequencing, an unbiased and high-throughput method of
sequencing multiple genomes.<sup><xref ref-type="bibr" rid="ref26">26</xref>−<xref ref-type="bibr" rid="ref28">28</xref></sup> The findings were
publicly disclosed, and the sequence was added to the GenBank sequence repository on
January 10, 2020.<sup><xref ref-type="bibr" rid="ref26">26</xref>,<xref ref-type="bibr" rid="ref27">27</xref></sup>Since then, more than 1000 sequences have been made available on the Global Initiative on
Sharing All Influenza Data (GISAID) and GenBank by researchers across the
globe.<sup><xref ref-type="bibr" rid="ref29">29</xref>,<xref ref-type="bibr" rid="ref30">30</xref></sup>According to the joint report by the World Health Organization (WHO) and China, 104
strains of the SARS-CoV-2 virus were isolated and sequenced using Illumina and Oxford
nanopore sequencing from the end of December 2019 to mid-February
2020.<sup><xref ref-type="bibr" rid="ref2">2</xref>,<xref ref-type="bibr" rid="ref4">4</xref></sup> Illumina
sequencing is a sequence-by-synthesis method using solid-phase bridge amplification,
whereras nanopore sequencing involves translocating a DNA molecule through a protein pore
and measuring subsequent shifts in voltage to determine the DNA sequence.<sup><xref ref-type="bibr" rid="ref31">31</xref></sup> Genome sequencing is important for researchers to design primers and probe
sequences for PCR and other nucleic acid tests.</p></sec><sec id="sec1.3"><title>Nucleic Acid Testing</title><sec id="sec1.3.1"><title>Designing a Nucleic Acid Test for SARS-CoV-2</title><p>Nucleic acid testing is the primary method of diagnosing COVID-19.<sup><xref ref-type="bibr" rid="ref32">32</xref></sup>A number of reverse transcription polymerase chain reaction (RT-PCR) kits have been
designed to detect SARS-CoV-2 genetically (<xref rid="tbl1" ref-type="other">Table <xref rid="tbl1" ref-type="other">1</xref></xref>). RT-PCR involves the reverse transcription of SARS-CoV-2 RNA into
complementary DNA (cDNA) strands, followed by amplification of specific regions of the
cDNA.<sup><xref ref-type="bibr" rid="ref33">33</xref>,<xref ref-type="bibr" rid="ref34">34</xref></sup> The
design process generally involves two main steps: (1) sequence alignment and primer
design, and (2) assay optimization and testing. Corman <italic>et al</italic>. aligned
and analyzed a number of SARS-related viral genome sequences to design a set of primers
and probes.<sup><xref ref-type="bibr" rid="ref35">35</xref></sup> Among the SARS-related viral genomes, they discovered
three regions that had conserved sequences: (1) the <italic>RdRP</italic> gene
(RNA-dependent RNA polymerase gene) in the open reading frame ORF1ab region, (2) the
<italic>E</italic> gene (envelope protein gene), and (3) the <italic>N</italic> gene
(nucleocapsid protein gene). Both the <italic>RdRP</italic> and <italic>E</italic> genes
had high analytical sensitivity for detection (technical limit of detection of 3.6 and
3.9 copies per reaction), whereas the <italic>N</italic> gene provided poorer analytical
sensitivity (8.3 copies per reaction). The assay can be designed as a two-target system,
where one primer universally detects numerous coronaviruses including SARS-CoV-2 and a
second primer set only detects SARS-CoV-2.</p><table-wrap id="tbl1" position="float"><label>Table 1</label><caption><title>Polymerase Chain Reaction (PCR) Tests/Primers for SARS-CoV-2</title></caption><graphic xlink:href="nn0c02624_0005" id="GRAPHIC-d7e629-autogenerated" position="float"></graphic></table-wrap><p>After designing the primers and probes, the next step involves optimizing assay
conditions (<italic>e</italic>.<italic>g</italic>., reagent conditions, incubation
times, and temperatures), followed by PCR testing. RT-PCR can be performed in either a
one-step or a two-step assay. In a one-step assay, reverse transcription and PCR
amplification are consolidated into one reaction. This assay format can provide rapid
and reproducible results for high-throughput analysis. The challenge is the difficulty
in optimizing the reverse transcription and amplification steps as they occur
simultaneously, which leads to lower target amplicon generation. In the two-step assay,
the reaction is done sequentially in separate tubes.<sup><xref ref-type="bibr" rid="ref36">36</xref></sup> This assay
format is more sensitive than the one-step assay, but it is more time-consuming and
requires optimizing additional parameters.<sup><xref ref-type="bibr" rid="ref36">36</xref>,<xref ref-type="bibr" rid="ref37">37</xref></sup> Lastly, controls need to be carefully selected to
ensure the reliability of the assay and to identify experimental errors.</p></sec><sec id="sec1.3.2"><title>Workflow for Nucleic Acid Testing for SARS-CoV-2</title><p>At least 11 nucleic-acid-based methods and eight antibody detection kits have been
approved in China by the National Medical Products Administration (NMPA) for detecting
SARS-CoV-2.<sup><xref ref-type="bibr" rid="ref38">38</xref></sup> However, RT-PCR is the most predominantly used method
for diagnosing COVID-19 using respiratory samples.<sup><xref ref-type="bibr" rid="ref2">2</xref>,<xref ref-type="bibr" rid="ref39">39</xref></sup> Upper respiratory samples are broadly
recommended, although lower respiratory samples are recommended for patients exhibiting
productive cough.<sup><xref ref-type="bibr" rid="ref40">40</xref></sup> Upper respiratory tract samples include
nasopharyngeal swabs, oropharyngeal swabs, nasopharyngeal washes, and nasal aspirates.
Lower respiratory tract samples include sputum, BAL fluid, and tracheal aspirates. Both
BAL and tracheal aspirates can be high risk for aerosol generation. The detectable viral
load depends on the days after illness onset. In the first 14 days after onset,
SARS-CoV-2 could most reliably be detected in sputum followed by nasal swabs, whereas
throat swabs were unreliable 8 days after symptom onset.<sup><xref ref-type="bibr" rid="ref41">41</xref>,<xref ref-type="bibr" rid="ref42">42</xref></sup> Given the variability in the
viral loads, a negative test result from respiratory samples does not rule out the
disease. These negatives could result from improper sampling techniques, low viral load
in the area sampled, or mutations in the viral genome.<sup><xref ref-type="bibr" rid="ref3">3</xref>,<xref ref-type="bibr" rid="ref43">43</xref></sup> Winichakoon <italic>et al</italic>.
recommended multiple lines of evidence for patients linked epidemiologically even if the
results are negative from nasopharyngeal and/or oropharyngeal swab.<sup><xref ref-type="bibr" rid="ref43">43</xref></sup></p><p>The United States Centers for Disease Control and Prevention (CDC) uses a one-step real
time RT-PCR (rRT-PCR) assay, which provides quantitative information on viral loads, to
detect the presence of SARS-CoV-2.<sup><xref ref-type="bibr" rid="ref44">44</xref></sup> To perform the assay, the viral
RNA is extracted and added to a master mix. The master mix contains nuclease-free water,
forward and reverse primers, a fluorophore-quencher probe, and a reaction mix
(consisting of reverse transcriptase, polymerase, magnesium, nucleotides, and
additives).<sup><xref ref-type="bibr" rid="ref32">32</xref></sup> The master mix and extracted RNA are loaded into a
PCR thermocycler, and the incubation temperatures are set to run the assay. The CDC has
recommended cycling conditions for rRT-PCR.<sup><xref ref-type="bibr" rid="ref44">44</xref></sup> During rRT-PCR, the
fluorophore-quencher probe is cleaved, generating a fluorescent signal. The fluorescent
signal is detected by the thermocycler, and the amplification progress is recorded in
real time. The probe sequence used by Guan <italic>et al</italic>. was Black Hole
Quencher-1 (BHQ1, quencher) and fluorescein amidite (FAM, fluorophore). This reaction
takes ∼45 min and can occur in a 96-well plate, where each well contains a
different sample or control. There must be both a positive and a negative control to
interpret the final results properly when running rRT-PCR. For SARS-CoV-2, the CDC
provides a positive control sequence called nCoVPC.<sup><xref ref-type="bibr" rid="ref44">44</xref></sup> A number of
SARS-CoV-2 RT-PCR primers and probes from different research groups and agencies are
listed in <xref rid="tbl1" ref-type="other">Table <xref rid="tbl1" ref-type="other">1</xref></xref>.</p></sec><sec id="sec1.3.3"><title>Integrating the Workflow for Nucleic Acid Detection with Clinical Management</title><p>There are different implementation workflows for RT-PCR tests in clinical settings.
Corman <italic>et al</italic>. proposed a three-step workflow for the diagnosis of
SARS-CoV-2.<sup><xref ref-type="bibr" rid="ref45">45</xref></sup> They define the three steps as first line screening,
confirmation, and discriminatory assays. To maximize the number of infected patients
identified, the first step detects all SARS-related viruses by targeting different
regions of the <italic>E</italic> gene. If this test is positive, then they propose the
detection of the <italic>RdRP</italic> gene using two different primers and two
different probes. If these results are also positive, then they conduct the
discriminatory test with one of the two probe sequences.<sup><xref ref-type="bibr" rid="ref45">45</xref></sup> See <xref rid="tbl1" ref-type="other">Table <xref rid="tbl1" ref-type="other">1</xref></xref> (Charité, Germany). Chu <italic>et
al</italic>. proposed a slightly different assay workflow.<sup><xref ref-type="bibr" rid="ref46">46</xref></sup> They
screened samples using primers for the <italic>N</italic> gene and used those from the
<italic>ORFlb</italic> gene for confirmation. A diagnosis where the patient sample is
positive with <italic>N</italic> gene primer and negative with the
<italic>ORFlb</italic> gene would be inconclusive. In such situations, protein tests
(<italic>i</italic>.<italic>e</italic>., antibody tests) or sequencing would be
required to confirm the diagnosis.<sup><xref ref-type="bibr" rid="ref46">46</xref></sup></p></sec></sec><sec id="sec1.4"><title>Computed Tomography</title><p>Due to the shortage of kits and false negative rate of RT-PCR, the Hubei Province, China
temporarily used CT scans as a clinical diagnosis for COVID-19.<sup><xref ref-type="bibr" rid="ref47">47</xref></sup> Chest
CT scans are non-invasive and involve taking many X-ray measurements at different angles
across a patient’s chest to produce cross-sectional images.<sup><xref ref-type="bibr" rid="ref48">48</xref>,<xref ref-type="bibr" rid="ref49">49</xref></sup> The images are analyzed by
radiologists to look for abnormal features that can lead to a diagnosis.<sup><xref ref-type="bibr" rid="ref48">48</xref></sup> The imaging features of COVID-19 are diverse and depend on the stage of
infection after the onset of symptoms. For example, Bernheim <italic>et al</italic>. saw
more frequent normal CT findings (56%) in the early stages of the disease (0–2
days)<sup><xref ref-type="bibr" rid="ref50">50</xref></sup> with a maximum lung involvement peaking at around 10 days
after the onset of symptoms.<sup><xref ref-type="bibr" rid="ref51">51</xref></sup> The most common hallmark features of
COVID-19 include bilateral and peripheral ground-glass opacities (areas of hazy
opacity)<sup><xref ref-type="bibr" rid="ref52">52</xref></sup> and consolidations of the lungs (fluid or solid material
in compressible lung tissue).<sup><xref ref-type="bibr" rid="ref50">50</xref>,<xref ref-type="bibr" rid="ref51">51</xref></sup> De Wever <italic>et al</italic>. found that ground-glass opacities are
most prominent 0–4 days after symptom onset. As a COVID-19 infection progresses, in
addition to ground-glass opacities, crazy-paving patterns
(<italic>i</italic>.<italic>e</italic>., irregular-shaped paved stone pattern)
develop,<sup><xref ref-type="bibr" rid="ref51">51</xref></sup> followed by increasing consolidation of the
lungs.<sup><xref ref-type="bibr" rid="ref50">50</xref>,<xref ref-type="bibr" rid="ref51">51</xref></sup> Based
on these imaging features, several retrospective studies have shown that CT scans have a
higher sensitivity (86–98%) and improved false negative rates compared to
RT-PCR.<sup><xref ref-type="bibr" rid="ref3">3</xref>,<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref53">53</xref>,<xref ref-type="bibr" rid="ref54">54</xref></sup> The main caveat of using CT for COVID-19 is
that the specificity is low (25%) because the imaging features overlap with other viral
pneumonia.<sup><xref ref-type="bibr" rid="ref3">3</xref></sup></p><p>COVID-19 is currently diagnosed with RT-PCR and has been screened for with CT scans, but
each technique has its own drawbacks. There are three issues that have arisen with RT-PCR.
First, the availability of PCR reagent kits has not kept up with demand. Second, community
hospitals outside of urban cities lack the PCR infrastructure to accommodate high sample
throughput. Lastly, RT-PCR relies on the presence of detectable SARS-CoV-2 in the sample
collected. If an asymptomatic patient was infected with SARS-CoV-2 but has since
recovered, PCR would not identify this prior infection, and control measures would not be
enforced. Meanwhile, CT systems are expensive, require technical expertise, and cannot
specifically diagnose COVID-19. Other technologies need to be adapted to SARS-CoV-2 to
address these deficiencies.</p></sec><sec id="sec1.5"><title>Emerging Diagnostic Tests for COVID-19</title><p>According to the WHO, the immediate priority for COVID-19 diagnostics research is the
development of nucleic acid and protein tests and detection at the point-of-care.<sup><xref ref-type="bibr" rid="ref2">2</xref></sup> The longer-term priority is to integrate these tests into multiplex panels.
In order to improve surveillance efforts, serological tests using proteins are needed in
addition to nucleic acid tests. These tests have the benefits of detection after recovery,
unlike nucleic acid tests. This enables clinicians to track both sick and recovered
patients, providing a better estimate of total SARS-CoV-2 infections. Point-of-care tests
are cost-effective, hand-held devices used to diagnose patients outside of centralized
facilities. These can be operated in areas like community centers to reduce the burden on
clinical laboratories.<sup><xref ref-type="bibr" rid="ref55">55</xref></sup></p></sec><sec id="sec1.6"><title>Nucleic Acid Testing</title><p>Nucleic acid tests using isothermal amplification are currently in development for
SARS-CoV-2 detection. Isothermal amplification techniques are conducted at a single
temperature and do not need specialized laboratory equipment to provide similar analytical
sensitivities to PCR.<sup><xref ref-type="bibr" rid="ref56">56</xref></sup> These techniques include recombinase polymerase
amplification, helicase-dependent amplification, and loop-mediated isothermal
amplification (LAMP). Several academic laboratories have developed and clinically tested
reverse transcription LAMP (RT-LAMP) tests for SARS-CoV-2.<sup><xref ref-type="bibr" rid="ref57">57</xref>−<xref ref-type="bibr" rid="ref60">60</xref></sup> RT-LAMP uses DNA
polymerase and four to six primers to bind to six distinct regions on the target genome.
In a four-primer system, there are two inner primers (a forward and a reverse inner
primer) and two outer primers; LAMP is highly specific because it uses a higher number of
primers.<sup><xref ref-type="bibr" rid="ref61">61</xref></sup> In LAMP diagnostic tests, a patient sample is added to the
tube, and the amplified DNA is detected by turbidity (a byproduct of the reaction), color
(addition of a pH-sensitive dye), or fluorescence (addition of a fluorescent dye that
binds to double-stranded DNA).<sup><xref ref-type="bibr" rid="ref62">62</xref></sup> The reaction occurs in <1 h at
60–65 °C with an analytical limit of detection of ∼75 copies per
μL. The approach is simple to operate, easy to visualize for detection, has less
background signal, and does not need a thermocycler.<sup><xref ref-type="bibr" rid="ref61">61</xref></sup> The drawbacks to
LAMP are the challenges of optimizing primers and reaction conditions. Other isothermal
amplification techniques for COVID-19 detection are in development.<sup><xref ref-type="bibr" rid="ref62">62</xref></sup></p><p>Isothermal amplification techniques can be multiplexed at the amplification and/or
readout stage. Multiplexing can use polymeric beads encoded with unique optical signatures
(<italic>e</italic>.<italic>g</italic>., organic fluorescent molecules) for barcoding.
Barcodes can be designed for different biomarkers in panels to detect multiple analytes
from a single patient sample in one reaction tube.<sup><xref ref-type="bibr" rid="ref63">63</xref></sup> Multiplexing
increases the amount of information gained from a single test and improves clinical
sensitivity and specificity.<sup><xref ref-type="bibr" rid="ref64">64</xref></sup> One way of encoding unique signatures is
through agents that emit fluorescent signals. Each unique emission codes for the capturing
DNA or antibody on the bead surface. A positive detection occurs when a patient’s
sample contains a sequence or antigen that links the bead’s capture molecule with a
secondary probe (labeled with fluorophore with a different emission than the beads). There
are barcoded-bead multiplex panels for diagnosing cystic fibrosis and respiratory
diseases.<sup><xref ref-type="bibr" rid="ref65">65</xref>,<xref ref-type="bibr" rid="ref66">66</xref></sup>Barcoded-bead assays/systems are engineered for laboratory use, but efforts are underway
to develop them for the point-of-care. However, the difficulty lies with the design of the
readout device. The complex barcode signal, which stems from the organic molecules,
requires a unique instrument design to discern the codes. Researchers are working on
overcoming this limitation by using inorganic quantum dots for barcoding, which enables
battery-operated excitation and a smartphone camera to capture the emission signal. Kim
<italic>et al</italic>. have demonstrated clinical specificity and sensitivity of 91%
and 95% using quantum dot barcodes for diagnosing patients with hepatitis B after
isothermal reverse polymerase amplification.<sup><xref ref-type="bibr" rid="ref63">63</xref></sup> Panels of barcodes can
be formulated into tablets for easy dissemination of reagents.<sup><xref ref-type="bibr" rid="ref67">67</xref></sup>Barcode panels can be designed for respiratory viruses, coronaviruses, sexually
transmitted diseases, and/or symptoms (<italic>e</italic>.<italic>g</italic>., fever,
cough, diarrhea).</p><p>In addition to isothermal amplification, there are other nucleic acid tests that could be
used for SARS-CoV-2 detection. SHERLOCK is a detection strategy that uses Cas13a
ribonuclease for RNA sensing.<sup><xref ref-type="bibr" rid="ref68">68</xref></sup> Viral RNA targets are reverse
transcribed to cDNA and isothermally amplified using reverse polymerase amplification. The
amplified products are transcribed back into RNA. Cas13a complexes with a RNA guide
sequence that binds with the amplified RNA product.<sup><xref ref-type="bibr" rid="ref69">69</xref></sup> Upon target
binding, Cas13a is activated. Cas13a then cleaves surrounding fluorophore-quencher probes
to produce a fluorescent signal. All components of SHERLOCK can be freeze-dried. Prior
studies using SHERLOCK could detect as few as 2000 copies/mL in clinical serum or urine
isolates for Zika virus.<sup><xref ref-type="bibr" rid="ref70">70</xref></sup> A SHERLOCK protocol for detecting SARS-CoV-2
has been released,<sup><xref ref-type="bibr" rid="ref71">71</xref></sup> and another Cas13a-based detection system has been
tested with SARS-CoV-2 clinical isolates.<sup><xref ref-type="bibr" rid="ref72">72</xref></sup></p></sec><sec id="sec1.7"><title>Protein Testing</title><p>Viral protein antigens and antibodies that are created in response to a SARS-CoV-2
infection can be used for diagnosing COVID-19. Changes in viral load over the course of
the infection may make viral proteins difficult to detect. For example, Lung <italic>et
al</italic>. showed high salivary viral loads in the first week after onset of symptoms,
which gradually declined with time.<sup><xref ref-type="bibr" rid="ref73">73</xref></sup> In contrast, antibodies generated
in response to viral proteins may provide a larger window of time for indirectly detecting
SARS-CoV-2. Antibody tests can be particularly useful for surveillance of COVID-19. One
potential challenge with developing accurate serological tests includes potential
cross-reactivity of SARS-CoV-2 antibodies with antibodies generated against other
coronaviruses. Lv <italic>et al</italic>. tested plasma samples from 15 COVID-19 patients
against the S protein of SARS-CoV-2 and SARS-CoV and saw a high frequency of
cross-reactivity.<sup><xref ref-type="bibr" rid="ref74">74</xref></sup></p><p>Currently, serological tests (<italic>i</italic>.<italic>e</italic>., blood tests for
specific antibodies) are in development.<sup><xref ref-type="bibr" rid="ref75">75</xref>−<xref ref-type="bibr" rid="ref77">77</xref></sup> Zhang
<italic>et al</italic>. detected immunoglobulin G and M (IgG and IgM) from human serum
of COVID-19 patients using an enzyme-linked immunosorbent assay (ELISA).<sup><xref ref-type="bibr" rid="ref75">75</xref></sup> They used the SARS-CoV-2 Rp3 nucleocapsid protein, which has 90% amino
acid sequence homology to other SARS-related viruses. The recombinant proteins adsorb onto
the surface of 96-well plates, and the excess protein is washed away. Diluted human serum
is added for 1 h, after which the plate is washed again. Antihuman IgG functionalized with
horseradish peroxidase is added and allowed to bind to the target. The plate is washed,
followed by the addition of the substrate 3,3′,5,5′-tetramethylbenzidine.
The peroxidase reacts with the substrate to cause a color change that can be detected by a
plate reader. If anti-SARS-CoV-2 IgG is present, it will be sandwiched between the
adsorbed nucleoprotein and the antihuman IgG probe, resulting in a positive signal. The
IgM test by Zhang <italic>et al</italic>. has a similar structure but uses antihuman IgM
adsorbed to the plate and an anti-Rp3 nucleocapsid probe. They tested 16 SARS-CoV-2
positive patient samples (confirmed by RT-PCR) and found the levels of these antibodies
increased over the first 5 days after symptom onset. On day zero, 50% and 81% of patients
were positive for IgM and IgG, respectively, but this increased to 81% and 100% at day
five.<sup><xref ref-type="bibr" rid="ref75">75</xref></sup> Antibodies were detected in respiratory, blood, or fecal
samples. Xiang <italic>et al</italic>. also detected SARS-CoV-2 IgG and IgM antibodies in
suspected cases.<sup><xref ref-type="bibr" rid="ref76">76</xref></sup> Given the recent studies, there may also be other
protein or cellular markers that can be used for detection. Guan <italic>et al</italic>.
showed that infected patients had elevated levels of C-reactive protein and D-dimer as
well as low levels of lymphocytes, leukocytes, and blood platelets.<sup><xref ref-type="bibr" rid="ref25">25</xref></sup>The challenge of using these biomarkers is that they are also abnormal in other illnesses.
A multiplex test with both antibody and small molecule markers could improve
specificity.</p></sec><sec id="sec1.8"><title>Point-of-Care Testing</title><p>Point-of-care tests are used to diagnose patients without sending samples to centralized
facilities, thereby enabling communities without laboratory infrastructure to detect
infected patients. Lateral flow antigen detection for SARS-CoV-2 is one point-of-care
approach under development for diagnosing COVID-19.<sup><xref ref-type="bibr" rid="ref76">76</xref></sup> In commercial
lateral flow assays, a paper-like membrane strip is coated with two lines: gold
nanoparticle-antibody conjugates are present in one line and capture antibodies in the
other. The patient’s sample (<italic>e</italic>.<italic>g</italic>., blood and
urine) is deposited on the membrane, and the proteins are drawn across the strip by
capillary action. As it passes the first line, the antigens bind to the gold
nanoparticle-antibody conjugates, and the complex flows together through the membrane. As
they reach the second line, the complex is immobilized by the capture antibodies, and a
red or blue line becomes visible. Individual gold nanoparticles are red in color, but a
solution containing clustered gold nanoparticles is blue due to the coupling of the
plasmon band. The lateral flow assay has demonstrated a clinical sensitivity, specificity,
and accuracy of 57%, 100%, and 69% for IgM and 81%, 100%, and 86% for IgG, respectively. A
test that detects both IgM and IgG yields a clinical sensitivity of 82%.<sup><xref ref-type="bibr" rid="ref76">76</xref></sup> Nucleic acid testing can also be incorporated into the lateral flow assay.
Previously, a RT-LAMP test was combined with lateral flow readout to detect
MERS-CoV.<sup><xref ref-type="bibr" rid="ref78">78</xref></sup> These tests are single use and suffer from poor
analytical sensitivity in comparison to RT-PCR. To improve the assay readout signal,
researchers have developed a variety of signal amplifying techniques
(<italic>e</italic>.<italic>g</italic>., thermal imaging and assembly of multiple gold
nanoparticles).<sup><xref ref-type="bibr" rid="ref79">79</xref></sup></p><p>Another approach for use at the point-of-care is microfluidic devices. These devices
consist of a palm-sized chip etched with micrometer-sized channels and reaction chambers.
The chip mixes and separates liquid samples using electrokinetic, capillary, vacuum,
and/or other forces. These chips can be constructed with materials such as polydimethyl
sulfoxide, glass, or paper. The key advantages of using microfluidics include
miniaturization, small sample volume, rapid detection times, and portability.<sup><xref ref-type="bibr" rid="ref80">80</xref></sup> Laksanasopin <italic>et al</italic>. developed a microfluidics-based
smartphone attachment to detect antibodies against three sexually transmitted infections
by sequentially moving reagents prestored on a cassette. The platform showed 100% and 87%
clinical sensitivity and specificity for HIV, respectively, when tested on 96 patients in
Rwanda.<sup><xref ref-type="bibr" rid="ref81">81</xref></sup> These technologies can be adapted to detect SARS-CoV-2 RNA
or proteins.</p><p>Here, we described some diagnostic technologies that have shown clinical feasibility.
<xref rid="tbl2" ref-type="other">Table <xref rid="tbl2" ref-type="other">2</xref></xref> provides a more extensive list of
emerging technologies that can be adapted for detecting SARS-CoV-2. There are many
platforms being developed in academic laboratories such as electrochemical sensors,
paper-based systems, and surface-enhanced Raman scattering based systems. Such approaches
are in the early stages of development and cannot be used to diagnose COVID-19
immediately. One can delineate diagnostic technology development into four phases (<xref rid="fig3" ref-type="fig">Figure <xref rid="fig3" ref-type="fig">3</xref></xref>). Phase 1 refers to technologies that are at
the proof-of-concept stage where researchers use synthetic targets to validate the
concept. Phase 2 refers to technologies that have analyzed a small number of patient
samples (<italic>i</italic>.<italic>e</italic>., <100 samples). Phase 3 typically
refers to technologies that advance to clinical trials with a large patient cohort. Phase
4 is when the technology is commercialized and used in patients. These emerging
technologies could play a role in detecting future outbreaks.</p><fig id="fig3" position="float"><label>Figure 3</label><caption><p>Developmental phases of diagnostic tests. Phases 1 and 2 typically occur in an
academic setting, while phases 3 and 4 occur in a company after commercial transfer.
Most diagnostic technologies are at the proof-of-concept stage, and few are in phase 3
that can be quickly adapted for diagnosing pathogens in new outbreaks. The advancement
of more phase 2 technologies into phase 3 would increase the number of approaches for
detecting new pathogens.</p></caption><graphic xlink:href="nn0c02624_0003" id="gr3" position="float"></graphic></fig><table-wrap id="tbl2" position="float"><label>Table 2</label><caption><title>Emerging Diagnostics Being Developed for SARS-CoV-2</title></caption><table frame="hsides" rules="groups" border="0"><colgroup><col></col><col></col><col></col><col></col><col></col><col></col><col></col></colgroup><thead><tr><th style="border:none;" align="center">platform</th><th style="border:none;" align="center">biomarker</th><th style="border:none;" align="center">POC (Y/N)</th><th style="border:none;" align="center">type of technology</th><th style="border:none;" align="center">how it works</th><th style="border:none;" align="center">types of clinical sample</th><th style="border:none;" align="center">clinical sample tested</th></tr></thead><tbody><tr><td style="border:none;">CRISPR<sup><xref ref-type="bibr" rid="ref109">109</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">Y</td><td style="border:none;">RPA</td><td style="border:none;">PCR, perform CRISPR/Ca9-mediated lateral flow nucleic assay
(CASLFA)</td><td style="border:none;">serum</td><td style="border:none;">110</td></tr><tr><td style="border:none;">CRISPR<sup><xref ref-type="bibr" rid="ref68">68</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">Y</td><td style="border:none;">RT-RPA</td><td style="border:none;">RPA, SHERLOCK multiplexed signal detection <italic>via</italic>fluorescence</td><td style="border:none;">nasopharyngeal swabs</td><td style="border:none;">384</td></tr><tr><td style="border:none;">LAMP<sup><xref ref-type="bibr" rid="ref110">110</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">LAMP</td><td style="border:none;">isothermal DNA synthesis using self-recurring strand displacement
reactions; positive detection leads to increased sample turbidity</td><td style="border:none;">throat swabs</td><td style="border:none;">53</td></tr><tr><td style="border:none;">RPA<sup><xref ref-type="bibr" rid="ref111">111</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">RPA</td><td style="border:none;">forward and reverse primars blind to DNA and amplify strands at
37 °C</td><td style="border:none;">fecal and nasal swabs</td><td style="border:none;">30</td></tr><tr><td style="border:none;">NASBA<sup><xref ref-type="bibr" rid="ref112">112</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">real-time NASBA</td><td style="border:none;">transcription-based amplification for RNA targets</td><td style="border:none;">nasal swabs</td><td style="border:none;">138</td></tr><tr><td style="border:none;">RCA<sup><xref ref-type="bibr" rid="ref113">113</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">rolling circle amplification</td><td style="border:none;">DNA polymerase used to extend a circular primer and repeatedly replicate
the sequence</td><td style="border:none;">serum</td><td style="border:none;">7</td></tr><tr><td style="border:none;">RT-LAMP<sup><xref ref-type="bibr" rid="ref114">114</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">LAMP</td><td style="border:none;">reverse transcriptase LAMP reaction for RNA targets</td><td style="border:none;">nasopharyngeal aspirates</td><td style="border:none;">59</td></tr><tr><td style="border:none;">smartphone dongle<sup><xref ref-type="bibr" rid="ref81">81</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">Y</td><td style="border:none;">ELISA</td><td style="border:none;">microfluidics-based cassette operating an ELISA</td><td style="border:none;">blood</td><td style="border:none;">96</td></tr><tr><td style="border:none;">quantum dot barcode<sup><xref ref-type="bibr" rid="ref63">63</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">Y</td><td style="border:none;">barcode</td><td style="border:none;">multiplexed quantum beads capture viral DNA for RPA detection</td><td style="border:none;">serum</td><td style="border:none;">72</td></tr><tr><td style="border:none;">magnetic bead<sup><xref ref-type="bibr" rid="ref115">115</xref></sup></td><td style="border:none;">nucleic acid</td><td style="border:none;">N</td><td style="border:none;">magnetic</td><td style="border:none;">magnetic beads isolate bacteria for PCR detection</td><td style="border:none;">stool</td><td style="border:none;">17</td></tr><tr><td style="border:none;">paramagnetic bead<sup><xref ref-type="bibr" rid="ref116">116</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">N</td><td style="border:none;">magnetic biosensor</td><td style="border:none;">magnetic separation of protein targets</td><td style="border:none;">serum</td><td style="border:none;">12</td></tr><tr><td style="border:none;">magnetic bead isolation<sup><xref ref-type="bibr" rid="ref117">117</xref></sup></td><td style="border:none;">whole bacteria</td><td style="border:none;">N</td><td style="border:none;">magnetic separation</td><td style="border:none;">magnetic isolaation of bacteria</td><td style="border:none;">synovia</td><td style="border:none;">12</td></tr><tr><td style="border:none;">ELISA<sup><xref ref-type="bibr" rid="ref118">118</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">N</td><td style="border:none;">ELISA</td><td style="border:none;">enzymatic reaction to produce colored product in presence of target</td><td style="border:none;">serum</td><td style="border:none;">30</td></tr><tr><td style="border:none;">SIMOA<sup><xref ref-type="bibr" rid="ref119">119</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">N</td><td style="border:none;">digital ELISA</td><td style="border:none;">digital readout of colored product by enzymatic reaction in presence of
target</td><td style="border:none;">serum</td><td style="border:none;">30</td></tr><tr><td style="border:none;">biobarcode assay<sup><xref ref-type="bibr" rid="ref120">120</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">N</td><td style="border:none;">DNA-assisted immunoassay</td><td style="border:none;">protein signal is indirectly detected by amplifying DNA conjugated to gold
nanoparticle</td><td style="border:none;">serum</td><td style="border:none;">18</td></tr><tr><td style="border:none;">rapid antigen test<sup><xref ref-type="bibr" rid="ref121">121</xref></sup></td><td style="border:none;">protein</td><td style="border:none;">Y</td><td style="border:none;">lateral flow</td><td style="border:none;">gold-coated antibodies produce colorimetric signal on paper in presence of
target</td><td style="border:none;">serum</td><td style="border:none;">117</td></tr></tbody></table></table-wrap></sec><sec id="sec1.9"><title>Smartphone Surveillance of Infectious Diseases</title><p>Controlling epidemics requires extensive surveillance, sharing of epidemiological data,
and patient monitoring.<sup><xref ref-type="bibr" rid="ref82">82</xref>,<xref ref-type="bibr" rid="ref83">83</xref></sup> Healthcare entities, from local hospitals to the WHO, require tools
that can improve the speed and ease of communication to manage the spread of diseases.
Smartphones can be leveraged for this purpose as they possess the connectivity,
computational power, and hardware to facilitate electronic reporting, epidemiological
databasing, and point-of-care testing (<xref rid="fig4" ref-type="fig">Figure <xref rid="fig4" ref-type="fig">4</xref></xref>).<sup><xref ref-type="bibr" rid="ref84">84</xref>,<xref ref-type="bibr" rid="ref85">85</xref></sup> An
exponential rise in worldwide smartphone adoption, including in sub-Saharan Africa, makes
smartphones a widely accessible technology to coordinate responses during large outbreaks
like COVID-19.<sup><xref ref-type="bibr" rid="ref84">84</xref></sup></p><fig id="fig4" position="float"><label>Figure 4</label><caption><p>Role of smartphones in diagnostics. Smartphone capabilities such as connectivity,
databasing, and onboard hardware enable better evidence-based policy making, national
disease response coordination, and community healthcare.</p></caption><graphic xlink:href="nn0c02624_0004" id="gr4" position="float"></graphic></fig><p>The global spread of COVID-19 has been catalyzed by insufficient communication and
underreporting.<sup><xref ref-type="bibr" rid="ref86">86</xref>,<xref ref-type="bibr" rid="ref87">87</xref></sup> A notable example is Iran, which confirmed its first 43 cases by
February 23, 2020, a case fatality rate of 19% (8 deaths), and 3 exported cases of Iranian
origin. From this reporting, transmission modeling suggests the number of infected
individuals in Iran was in the thousands.<sup><xref ref-type="bibr" rid="ref88">88</xref></sup> Smartphones can be paired
with pre-existing diagnostic tests to provide real-time geospatial information that
empowers national and global health agencies to implement coordinated control strategies.
Several research groups have used smartphones for geospatial tracking of infectious
diseases such as HIV, Ebola, and tuberculosis.<sup><xref ref-type="bibr" rid="ref89">89</xref>−<xref ref-type="bibr" rid="ref91">91</xref></sup> For Ebola, smartphones were used for contact tracing, which is the
practice of tracking and identifying people that have come into contact with infected
patients and may also be infected.<sup><xref ref-type="bibr" rid="ref89">89</xref></sup> Smartphones can digitize the
process of contact tracing to provide more complete and shareable records.</p><p>Without communication between regional healthcare agencies, transmission rates can vary
across a country,<sup><xref ref-type="bibr" rid="ref92">92</xref></sup> such as occurred during the 2003 SARS outbreak in
Canada. Toronto, Ontario had 247 cases, 3 of which were imported, whereas Vancouver,
British Columbia had only 5 cases, 4 of which were imported.<sup><xref ref-type="bibr" rid="ref92">92</xref>,<xref ref-type="bibr" rid="ref93">93</xref></sup> At the time, Ontario did not have a
provincial public health agency, but British Columbia’s agency previously
identified that the province was at risk of importing emerging infectious diseases. Prior
to the SARS outbreak, British Columbia’s public health agency established a digital
network to facilitate communication across the province.<sup><xref ref-type="bibr" rid="ref92">92</xref></sup> These
communication networks can be expanded by leveraging smartphone connectivity. Smartphones
can be used in the field to upload and share epidemiological data onto public health
databases and to coordinate outbreak responses.</p><p>People suspected to have COVID-19 can encounter communication barriers with their
healthcare providers. Anyone exhibiting mild respiratory symptoms may be hesitant to
travel to overcrowded hospitals, as they face an increased risk of contracting COVID-19.
Smartphones can be leveraged to provide a direct line of communication between patients
and clinicians without risking infection of either party. During the 2009 influenza
pandemic, Switzerland used medical teleconsultations to manage suspected cases in addition
to its existing reporting system.<sup><xref ref-type="bibr" rid="ref94">94</xref></sup> Teleconsultations led to higher
total reporting of influenza compared to in-person consultations due to the lower barrier
of access. If COVID-19 infected individuals visit a hospital and test positive, patients
with mild symptoms are sent home for self-quarantine.<sup><xref ref-type="bibr" rid="ref95">95</xref></sup> Self-quarantine
naturally deters communication between patients and clinicians, resulting in adverse
mental health effects for the patient and reduced monitoring by the clinician. Smartphone
apps can connect patients with mental health counselors to cope with isolation and fear
during disease outbreaks and self-quarantine.<sup><xref ref-type="bibr" rid="ref96">96</xref>,<xref ref-type="bibr" rid="ref97">97</xref></sup> In addition, patients can self-report symptoms and
behaviors, facilitating remote monitoring by clinicians.<sup><xref ref-type="bibr" rid="ref98">98</xref></sup>Smartphone-based reporting also provides epidemiologists with information relevant to
potential transmission mechanisms. For example, during the 2013 MERS outbreak, a
smartphone app was used to monitor travelers during their Hajj pilgrimage. In the app,
users reported hand hygiene protocols, animal contact, and onset of symptoms, both during
the pilgrimage and after returning to their home countries.<sup><xref ref-type="bibr" rid="ref99">99</xref></sup> Similar
apps can be used to keep public health agencies actively informed and to improve responses
to disease outbreaks.</p><p>In recent years, there have been significant developments in integrating smartphones and
diagnostic technologies. Smartphone components (<italic>e</italic>.<italic>g</italic>.,
camera, flashlight, and audio jack) have been used for the readout of diagnostic assays in
place of conventional laboratory equipment.<sup><xref ref-type="bibr" rid="ref100">100</xref></sup> These devices can
simplify diagnostic workflow by automating readout and databasing. For example, a
smartphone-based microscope was field tested in Cameroon and demonstrated faster
turnaround times than standard techniques.<sup><xref ref-type="bibr" rid="ref101">101</xref></sup> Kanazawa <italic>et
al</italic>. validated the use of smartphones accompanied by forward looking infrared
radar (FLIR) for the thermal detection of body temperature due to inflammation. This
technology may also be adapted for the detection of fever, a common symptom of many
coronaviruses including COVID-19.<sup><xref ref-type="bibr" rid="ref102">102</xref></sup> Mudanyali <italic>et al</italic>.
also developed a smartphone-based microscope that transfers diagnostic results to a
database for analysis and spatiotemporal mapping.<sup><xref ref-type="bibr" rid="ref103">103</xref></sup> These devices can
help address the need for point-of-care testing at the community level, where there is
underreporting.</p></sec><sec id="sec2"><title>Conclusions and Outlook</title><p>The availability of established diagnostic technologies (phase 3, <xref rid="fig3" ref-type="fig">Figure
<xref rid="fig3" ref-type="fig">3</xref></xref>) have enabled researchers to plug-and-play in the design of
COVID-19 diagnostics. Such technologies took decades to optimize, but they are now playing
an important role in identifying and managing the spread of COVID-19. Lessons learned from
the 2002 SARS outbreak have guided the development of COVID-19 identification and detection.
Transmission electron microscopy was used to identify the morphology of the virus, genome
sequencing was used to confirm the identity of the virus, and sequence data were used to
help design PCR primers and probes. SARS-CoV took 5 months to be identified. The same
techniques were used to identify SARS-CoV-2 in only 3 weeks.<sup><xref ref-type="bibr" rid="ref104">104</xref></sup> The rapid
identification and sequencing of SARS-CoV-2 has enabled the rapid development of nucleic
acid tests. These approaches provide a first line of defense against an outbreak. The next
step being worked on is to establish serological tests because they are easier to administer
and may complement nucleic acid tests for diagnosing COVID-19 infection. There is now a call
for development of point-of-care tests and multiplex assays. Technologies that are
straddling phases 2 and 3 (<xref rid="fig3" ref-type="fig">Figure <xref rid="fig3" ref-type="fig">3</xref></xref>) such as
isothermal amplification, barcoding, and microfluidic technologies should be further
developed so that they can become plug-and-play systems and can be rapidly implemented in an
outbreak situation. The combination of diagnostics and smartphones should provide greater
communication and surveillance. In conclusion, diagnostics are an important part of the
toolbox for dealing with outbreaks because they enable healthcare workers to direct
resources and efforts to patients with COVID-19. This process can curb the spread of
infectious pathogens and reduce mortality.</p><p>Of note, data on COVID-19 are evolving quickly. Some of the specifics in this review may
change as more studies become available. Many highlighted studies also identified weaknesses
with their experimental study and design. Some referenced manuscripts are preprints and have
not been peer-reviewed.</p></sec></body><back><notes notes-type="" id="notes1"><title>Author Contributions</title><p><sup>◆</sup> These authors contributed equally to the work.</p></notes><notes notes-type="COI-statement" id="NOTES-d7e1385-autogenerated"><p>The authors declare the following competing financial interest(s): Co-founded companies
Luna Nanotech and Vigilant.</p></notes><ack><title>Acknowledgments</title><p>W.C.W.C., S.M., and J.B.G. acknowledge the Canadian Institutes of Health Research and
Natural Sciences and Engineering Research Council of Canada through the Collaborative Health
Research Program (CPG-158269 and 2015-06397). W.C.W.C. acknowledges the Canadian Research
Chairs Program (950-223824). We also acknowledge the Natural Sciences and Engineering
Research Council of Canada Postgraduate Scholarship (B.U.), the Barbara and Frank Milligan
Graduate Fellowship (B.U., H.N.K., and P.K.), the Paul Cadario Doctoral Fellowship in Global
Engineering (B.U., M.O., and H.N.K.), McCuaig-Throop Bursary for support (B.U.), Canadian
Institutes of Health Research Vanier Scholarship (H.N.K.), the University of Toronto
MD/Ph.D. program (H.N.K.), and National Research Council - Center for Research and
Applications in Fluidic Technologies Fellowship (A. M.).</p></ack><glossary id="dl1"><def-list><title>Glossary</title><def-item><term><bold>Angiotensin converting enzyme 2 (ACE2) receptor</bold></term><def><p>the cell receptor likely responsible for SARS-CoV-2 viral entry into cells</p></def></def-item><def-item><term><bold>COVID-19</bold></term><def><p>Coronavirus disease 2019</p></def></def-item><def-item><term><bold>bronchoalveolar lavage (BAL) fluid</bold></term><def><p>fluid collected using a bronchoscope (<italic>i</italic>.<italic>e</italic>., procedure
that looks at lungs and air passage) that is used to diagnose a lung infection</p></def></def-item><def-item><term><bold>computed tomography (CT)</bold></term><def><p>a non-invasive form of medical imaging that compiles cross-sectional images of the
body</p></def></def-item><def-item><term><bold><italic>Coronaviridae</italic></bold><bold>family</bold></term><def><p>family of enveloped viruses with positive-sense single-stranded RNA</p></def></def-item><def-item><term><bold>enzyme-linked immunosorbent assay (ELISA)</bold></term><def><p>a laboratory technique that quantifies proteins, peptides, antibodies, or hormones from
the biological system</p></def></def-item><def-item><term><bold>isothermal amplification</bold></term><def><p>genetic multiplication techniques that occur at a single temperature</p></def></def-item><def-item><term><bold>lateral flow assays</bold></term><def><p>rapid, paper-based platforms that detect analytes at the point-of-care (most commonly
of antigens and antibodies)</p></def></def-item><def-item><term><bold>loop-mediated isothermal amplification (LAMP)</bold></term><def><p>an isothermal amplification technique commonly used for point-of-care testing. LAMP
involves 4–6 primers and exponential amplification (genetic multiplication) of
target DNA that produces concatenated DNA structures for detection</p></def></def-item><def-item><term><bold>microfluidics</bold></term><def><p>technologies that manipulate and control fluids at the microscale
(10<sup>–6</sup> m) or smaller</p></def></def-item><def-item><term><bold>multiplexing</bold></term><def><p>simultaneous testing of multiple target molecules in a single sample</p></def></def-item><def-item><term><bold>nasopharyngeal swabs</bold></term><def><p>an elongated swab that collects secretions from the back of the patient’s
nose</p></def></def-item><def-item><term><bold>oropharyngeal swabs</bold></term><def><p>a swab that collects secretions from the patient’s throat</p></def></def-item><def-item><term><bold>point-of-care testing</bold></term><def><p>diagnostic testing performed at or near the site of the patient</p></def></def-item><def-item><term><bold>reverse transcription polymerase chain reaction (RT-PCR)</bold></term><def><p>a nucleic acid amplification technique where RNA is converted into DNA and repeatedly
multiplied for detection</p></def></def-item><def-item><term><bold>severe acquired respiratory syndrome coronavirus 2 (SARS-CoV-2)</bold></term><def><p>the name of the infective virus causing the COVID-19 disease</p></def></def-item><def-item><term><bold>serological testing</bold></term><def><p>diagnostic testing that measures the level of antibodies in blood</p></def></def-item><def-item><term><bold>sputum</bold></term><def><p>a mixture of saliva and mucus from the respiratory tract of a patient</p></def></def-item><def-item><term><bold>surveillance</bold></term><def><p>the collection and analysis of data for the prevention and control of a disease</p></def></def-item><def-item><term><bold>tracheal aspirates</bold></term><def><p>tracheal secretions collected for culturing and pathogen detection</p></def></def-item></def-list></glossary><ref-list><title>References</title><ref id="ref1"><mixed-citation publication-type="journal" id="cit1"><name><surname>Zhou</surname><given-names>P.</given-names></name>; <name><surname>Yang</surname><given-names>X.-L.</given-names></name>; <name><surname>Wang</surname><given-names>X.-G.</given-names></name>; <name><surname>Hu</surname><given-names>B.</given-names></name>; <name><surname>Zhang</surname><given-names>L.</given-names></name>; <name><surname>Zhang</surname><given-names>W.</given-names></name>; <name><surname>Si</surname><given-names>H.-R.</given-names></name>; <name><surname>Zhu</surname><given-names>Y.</given-names></name>; <name><surname>Li</surname><given-names>B.</given-names></name>; <name><surname>Huang</surname><given-names>C.-L.</given-names></name>; et al. <article-title>A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat
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