The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Serology testing in the COVID-19 pandemic response
Open accessAbstract
The collapse of global cooperation and a failure of international solidarity have led to many low-income and middle-income countries being denied access to molecular diagnostics in the COVID-19 pandemic response. Yet the scarcity of knowledge on the dynamics of the immune response to infection has led to hesitation on recommending the use of rapid immunodiagnostic tests, even though rapid serology tests are commercially available and scalable. On the basis of our knowledge and understanding of viral infectivity and host response, we urge countries without the capacity to do molecular testing at scale to research the use of serology tests to triage symptomatic patients in community settings, to test contacts of confirmed cases, and in situational analysis and surveillance. The WHO R&D Blue Print expert group identified eight priorities for research and development, of which the highest is to mobilise research on rapid point-of-care diagnostics for use at the community level. This research should inform control programmes of the required performance and utility of rapid serology tests, which, when applied specifically for appropriate public health measures to then be put in place, can make a huge difference.
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
A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
Virological assessment of hospitalized patients with COVID-2019
Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661
Antibody responses to SARS-CoV-2 in patients with COVID-19
Long, Q.-X., Liu, B.-Z., Deng, H.-J., Wu, G.-C., Deng, K., Chen, Y.-K., Liao, P., Qiu, J.-F., Lin, Y., Cai, X.-F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0897-1 · Cited by 2,876
Antibody Responses to SARS-CoV-2 in Patients With Novel Coronavirus Disease 2019
Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., et al. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa344 · Cited by 2,607
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D., & Fraser, C. (2020) · Science · doi:10.1126/science.abb6936 · Cited by 2,512
What policy makers need to know about COVID-19 protective immunity
Altmann, D. M., Douek, D. C., & Boyton, R. J. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30985-5 · Cited by 223
Cited by
Diagnostics for COVID-19: moving from pandemic response to control
Peeling, R. W., Heymann, D. L., Teo, Y.-Y., & Garcia, P. J. (2022) · The Lancet · doi:10.1016/s0140-6736(21)02346-1 · Cited by 486
Self-citationScaling up COVID-19 rapid antigen tests: promises and challenges
Peeling, R. W., Olliaro, P. L., Boeras, D. I., & Fongwen, N. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00048-7 · Cited by 323
Self-citationAnand, S., Montez-Rath, M., Han, J., Bozeman, J., Kerschmann, R., Beyer, P., Parsonnet, J., & Chertow, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32009-2 · Cited by 265
The association between obesity and peak antibody titer response in COVID‐19 infection
Soffer, S., Glicksberg, B. S., Zimlichman, E., Efros, O., Levin, M. A., Freeman, R., Reich, D. L., & Klang, E. (2021) · Obesity · doi:10.1002/oby.23208 · Cited by 48
Metadata
- Authors
- Peeling, R. W., Wedderburn, C. J., Garcia, P. J., Boeras, D., Fongwen, N., Nkengasong, J., Sall, A., Tanuri, A., & Heymann, D. L.
- Venue
- The Lancet Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 20
- Issue
- 9
- Pages
- e245-e249
- ISSN
- 1473-3099 (ISSN-L)
- OpenAlex
- W3042918038
- Cited by
- 360
- References
- 6
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Serology testing in the COVID-19 pandemic response. https://app.cochat.ai/papers/10.1016/s1473-3099(20)30517-x
- The paper
- Peeling, R. W., Wedderburn, C. J., Garcia, P. J., Boeras, D., Fongwen, N., Nkengasong, J., Sall, A., Tanuri, A., & Heymann, D. L. (2020). Serology testing in the COVID-19 pandemic response. The Lancet Infectious Diseases. https://doi.org/10.1016/s1473-3099(20)30517-x
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.