The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Substantial underestimation of SARS-CoV-2 infection in the United States
Open accessAbstract
Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64–99%) of this difference is due to incomplete testing, while 14% (0.3–36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.
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
Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR
Corman, V. M., Landt, O., Kaiser, M., Molenkamp, R., Meijer, A., Chu, D. K., Bleicker, T., Brünink, S., Schneider, J., Schmidt, M. L., et al. (2020) · Eurosurveillance · doi:10.2807/1560-7917.es.2020.25.3.2000045 · Cited by 7,344
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,685
Detection of SARS-CoV-2 in Different Types of Clinical Specimens
Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., & Tan, W. (2020) · JAMA · doi:10.1001/jama.2020.3786 · Cited by 5,028
SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients
Zou, L., Ruan, F., Huang, M., Liang, L., Huang, H., Hong, Z., Yu, J., Kang, M., Song, Y., Xia, J., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001737 · Cited by 4,810
Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany
Rothe, C., Schunk, M., Sothmann, P., Bretzel, G., Froeschl, G., Wallrauch, C., Zimmer, T., Thiel, V., Janke, C., Guggemos, W., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmc2001468 · Cited by 4,144
Epidemiology of COVID-19 Among Children in China
Dong, Y., Mo, X., Hu, Y., Qi, X., Jiang, F., Jiang, Z., & Tong, S. (2020) · Pediatrics · doi:10.1542/peds.2020-0702 · Cited by 4,090
Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020) · Science · doi:10.1126/science.abb3221 · Cited by 3,633
Ferguson, N. M., Laydon, D. J., Nedjati-Gilani, G. L., Imai, N., Ainslie, K. E. C., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z. M., Cuomo-Dannenburg, G., et al. (2020) · Spiral (Imperial College London) · doi:10.25561/77482 · Cited by 3,281
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
Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period
Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H., & Lipsitch, M. (2020) · Science · doi:10.1126/science.abb5793 · Cited by 2,611
Cited by
Fatigue and cognitive impairment in Post-COVID-19 Syndrome: a systematic review and meta-analysis
Ceban, F., Ling, S., Lui, L. M. W., Lee, Y., Gill, H., Teopiz, K. M., Rodrigues, N. B., Subramaniapillai, M., Di Vincenzo, J. D., Cao, B., et al. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.12.020 · Cited by 1,554
Thomson, E. C., Rosen, L. E., Shepherd, J. G., Spreafico, R., da Silva Filipe, A., Wojcechowskyj, J. A., Davis, C., Piccoli, L., Pascall, D. J., Dillen, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.037 · Cited by 606
Three waves changes, new variant strains, and vaccination effect against COVID-19 pandemic
El-Shabasy, R. M., Nayel, M. A., Taher, M. M., Abdelmonem, R., Shoueir, K. R., & Kenawy, E. R. (2022) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2022.01.118 · Cited by 259
Estimating SARS-CoV-2 infections from deaths, confirmed cases, tests, and random surveys
Irons, N. J., & Raftery, A. E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2103272118 · Cited by 105
SARS-CoV-2 Infection Is at Herd Immunity in the Majority Segment of the Population of Qatar
Al-Thani, M. H., Farag, E., Bertollini, R., Al Romaihi, H. E., Abdeen, S., Abdelkarim, A., Daraan, F., Elhaj Ismail, A. I. H., Mostafa, N., Sahl, M., et al. (2021) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofab221 · Cited by 85
Metadata
- Authors
- Wu, S. L., Mertens, A. N., Crider, Y. S., Nguyen, A., Pokpongkiat, N. N., Djajadi, S., Seth, A., Hsiang, M. S., Colford, J. M., Reingold, A., Arnold, B. F., Hubbard, A., & Benjamin-Chung, J.
- Venue
- Nature Communications
- Published
- 2020-09-09
- Volume
- 11
- Issue
- 1
- Pages
- 4507-4507
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3084370551
- Cited by
- 355
- References
- 58
- FWCI
- 11.70
- Type
- journal article
- Language
- en
- Open access
- gold
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: Substantial underestimation of SARS-CoV-2 infection in the United States. https://app.cochat.ai/papers/10.1038/s41467-020-18272-4
- The paper
- Wu, S. L., Mertens, A. N., Crider, Y. S., Nguyen, A., Pokpongkiat, N. N., Djajadi, S., Seth, A., Hsiang, M. S., Colford, J. M., Reingold, A., Arnold, B. F., Hubbard, A., & Benjamin-Chung, J. (2020). Substantial underestimation of SARS-CoV-2 infection in the United States. Nature Communications. https://doi.org/10.1038/s41467-020-18272-4
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.
