The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk
Open accessAbstract
Summary Background The new Middle East respiratory syndrome coronavirus (MERS-CoV) infection shares many clinical, epidemiological, and virological similarities with that of severe acute respiratory syndrome (SARS)-CoV. We aimed to estimate virus transmissibility and the epidemic potential of MERS-CoV, and to compare the results with similar findings obtained for prepandemic SARS. Methods We retrieved data for MERS-CoV clusters from the WHO summary and subsequent reports, and published descriptions of cases, and took into account 55 of the 64 laboratory-confirmed cases of MERS-CoV reported as of June 21, 2013, excluding cases notified in the previous 2 weeks. To assess the interhuman transmissibility of MERS-CoV, we used Bayesian analysis to estimate the basic reproduction number (R0) and compared it to that of prepandemic SARS. We considered two scenarios, depending on the interpretation of the MERS-CoV cluster-size data. Results With our most pessimistic scenario (scenario 2), we estimated MERS-CoV R0 to be 0·69 (95% CI 0·50–0·92); by contrast, the R0 for prepandemic SARS-CoV was 0·80 (0·54–1·13). Our optimistic scenario (scenario 1) yielded a MERS-CoV R0 of 0·60 (0·42–0·80). Because of recent implementation of effective contact tracing and isolation procedures, further MERS-CoV transmission data might no longer describe an entire cluster, but only secondary infections directly caused by the index patient. Hence, we calculated that, under scenario 2, eight or more secondary infections caused by the next index patient would translate into a 5% or higher chance that the revised MERS-CoV R0 would exceed 1—ie, that MERS-CoV might have pandemic potential. Interpretation Our analysis suggests that MERS-CoV does not yet have pandemic potential. We recommend enhanced surveillance, active contact tracing, and vigorous searches for the MERS-CoV animal hosts and transmission routes to human beings. Funding Agence Nationale de la Recherche (Labex Integrative Biology of Emerging Infectious Diseases), and the European Community's Seventh Framework Programme project PREDEMICS.
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
Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC
Raj, V. S., Mou, H., Smits, S. L., Dekkers, D. H. W., Müller, M. A., Dijkman, R., Muth, D., Demmers, J. A. A., Zaki, A., Fouchier, R. A. M., et al. (2013) · Nature · doi:10.1038/nature12005 · Cited by 2,061
Adaptation of SARS Coronavirus to Humans
Holmes, K. V. (2005) · Science · doi:10.1126/science.1118817 · Cited by 63
Cited by
Wu, J. T., Leung, K., & Leung, G. M. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30260-9 · Cited by 4,389
Assiri, A., Al-Tawfiq, J. A., Al-Rabeeah, A. A., Al-Rabiah, F. A., Al-Hajjar, S., Al-Barrak, A., Flemban, H., Al-Nassir, W. N., Balkhy, H. H., Al-Hakeem, R. F., et al. (2013) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(13)70204-4 · Cited by 1,435
Saad, M., Omrani, A. S., Baig, K., Bahloul, A., Elzein, F., Matin, M. A., Selim, M. A. A., Mutairi, M. A., Nakhli, D. A., Aidaroos, A. Y. A., et al. (2014) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2014.09.003 · Cited by 486
Tang, X.-C., Agnihothram, S. S., Jiao, Y., Stanhope, J., Graham, R. L., Peterson, E. C., Avnir, Y., Tallarico, A. S. C., Sheehan, J., Zhu, Q., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402074111 · Cited by 232
Wang, L., Shi, W., Chappell, J. D., Joyce, M. G., Zhang, Y., Kanekiyo, M., Becker, M. M., van Doremalen, N., Fischer, R., Wang, N., et al. (2018) · Journal of Virology · doi:10.1128/jvi.02002-17 · Cited by 169
Metadata
- Authors
- Breban, R., Riou, J., & Fontanet, A.
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 382
- Issue
- 9893
- Pages
- 694-699
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- OpenAlex
- W2130227690
- Cited by
- 385
- References
- 2
- FWCI
- 39.04
- 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: Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk. https://app.cochat.ai/papers/10.1016/s0140-6736(13)61492-0
- The paper
- Breban, R., Riou, J., & Fontanet, A. (2013). Interhuman transmissibility of Middle East respiratory syndrome coronavirus: estimation of pandemic risk. The Lancet. https://doi.org/10.1016/s0140-6736(13)61492-0
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.