The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Diagnostics for SARS-CoV-2 detection: A comprehensive review of the FDA-EUA COVID-19 testing landscape
Open accessAbstract
The rapidly spreading outbreak of COVID-19 disease is caused by the SARS-CoV-2 virus, first reported in December 2019 in Wuhan, China. As of June 17, 2020, this virus has infected over 8.2 million people but ranges in symptom severity, making it difficult to assess its overall infection rate. There is a need for rapid and accurate diagnostics to better monitor and prevent the spread of COVID-19. In this review, we present and evaluate two main types of diagnostics with FDA-EUA status for COVID-19: nucleic acid testing for detection of SARS-CoV-2 RNA, and serological assays for detection of SARS-CoV-2 specific IgG and IgM patient antibodies, along with the necessary sample preparation for accurate diagnoses. In particular, we cover and compare tests such as the CDC 2019-nCoV RT-PCR Diagnostic Panel, Cellex's qSARS-CoV-2 IgG/IgM Rapid Test, and point-of-care tests such as Abbott's ID NOW COVID-19 Test. Antibody testing is especially important in understanding the prevalence of the virus in the community and to identify those who have gained immunity. We conclude by highlighting the future of COVID-19 diagnostics, which include the need for quantitative testing and the development of emerging biosensors as point-of-care tests.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30251-8 · Cited by 11,907
Coronaviridae Study Group of the International Committee on Taxonomy of Viruses, Gorbalenya, A. E., Baker, S. C., Baric, R. S., de Groot, R. J., Drosten, C., Gulyaeva, A. A., Haagmans, B. L., Lauber, C., Leontovich, A. M., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0695-z · Cited by 8,403
Lai, C.-C., Shih, T.-P., Ko, W.-C., Tang, H.-J., & Hsueh, P.-R. (2020) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2020.105924 · Cited by 6,068
Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19
Blanco-Melo, D., Nilsson-Payant, B. E., Liu, W.-C., Uhl, S., Hoagland, D., Møller, R., Jordan, T. X., Oishi, K., Panis, M., Sachs, D., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.026 · Cited by 4,222
Coronaviruses: An Overview of Their Replication and Pathogenesis
Fehr, A. R., & Perlman, S. (2015) · Methods in Molecular Biology · doi:10.1007/978-1-4939-2438-7_1 · Cited by 3,787
Engvall, E., & Perlmann, P. (1972) · PubMed · Cited by 3,107
Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections
Long, Q.-X., Tang, X.-J., Shi, Q.-L., Li, Q., Deng, H.-J., Yuan, J., Hu, J.-L., Xu, W., Zhang, Y., Lv, F.-J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0965-6 · Cited by 2,686
Seo, G., Lee, G., Kim, M. J., Baek, S.-H., Choi, M., Ku, K. B., Lee, C.-S., Jun, S., Park, D., Kim, H. G., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c02823 · Cited by 1,837
A serological assay to detect SARS-CoV-2 seroconversion in humans
Amanat, F., Stadlbauer, D., Strohmeier, S., Nguyen, T. H. O., Chromikova, V., McMahon, M., Jiang, K., Arunkumar, G. A., Jurczyszak, D., Polanco, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0913-5 · Cited by 1,769
Cited by
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00421-z · Cited by 75
Metadata
- Authors
- Ravi, N., Cortade, D. L., Ng, E., & Wang, S. X.
- Venue
- Biosensors and Bioelectronics
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 165
- Pages
- 112454
- ISSN
- 0956-5663 (ISSN-L)
- OpenAlex
- W3042554514
- Cited by
- 371
- References
- 46
- FWCI
- 9.81
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Diagnostics for SARS-CoV-2 detection: A comprehensive review of the FDA-EUA COVID-19 testing landscape. https://app.cochat.ai/papers/10.1016/j.bios.2020.112454
- The paper
- Ravi, N., Cortade, D. L., Ng, E., & Wang, S. X. (2020). Diagnostics for SARS-CoV-2 detection: A comprehensive review of the FDA-EUA COVID-19 testing landscape. Biosensors and Bioelectronics. https://doi.org/10.1016/j.bios.2020.112454
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
