The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Estimating the impact of school closure on influenza transmission from Sentinel data
Open accessAbstract
The threat posed by the highly pathogenic H5N1 influenza virus requires public health authorities to prepare for a human pandemic. Although pre-pandemic vaccines and antiviral drugs might significantly reduce illness rates, their stockpiling is too expensive to be practical for many countries. Consequently, alternative control strategies, based on non-pharmaceutical interventions, are a potentially attractive policy option. School closure is the measure most often considered. The high social and economic costs of closing schools for months make it an expensive and therefore controversial policy, and the current absence of quantitative data on the role of schools during influenza epidemics means there is little consensus on the probable effectiveness of school closure in reducing the impact of a pandemic. Here, from the joint analysis of surveillance data and holiday timing in France, we quantify the role of schools in influenza epidemics and predict the effect of school closure during a pandemic. We show that holidays lead to a 20–29% reduction in the rate at which influenza is transmitted to children, but that they have no detectable effect on the contact patterns of adults. Holidays prevent 16–18% of seasonal influenza cases (18–21% in children). By extrapolation, we find that prolonged school closure during a pandemic might reduce the cumulative number of cases by 13–17% (18–23% in children) and peak attack rates by up to 39–45% (47–52% in children). The impact of school closure would be reduced if it proved difficult to maintain low contact rates among children for a prolonged period.
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
Markov Chain Monte Carlo in Practice
(1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,757
Sequential Monte Carlo Methods in Practice
(2001) · Springer eBooks · doi:10.1007/978-1-4757-3437-9 · Cited by 7,366
Monte Carlo Strategies in Scientific Computing
Liu, J. S. (2004) · Springer Series in Statistics · doi:10.1007/978-0-387-76371-2 · Cited by 2,690
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,187
Self-citationMarkov Chain Monte Carlo in Practice
Gasparini, M. (1997) · Technometrics · doi:10.1080/00401706.1997.10485132 · Cited by 1,879
Monte Carlo Strategies in Scientific Computing
Hesterberg, T. (2002) · Technometrics · doi:10.1198/tech.2002.s85 · Cited by 1,409
Mitigation strategies for pandemic influenza in the United States
Germann, T. C., Kadau, K., Longini, I. M., & Macken, C. A. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601266103 · Cited by 1,047
The effect of public health measures on the 1918 influenza pandemic in U.S. cities
Bootsma, M. C. J., & Ferguson, N. M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0611071104 · Cited by 798
Self-citationPublic health interventions and epidemic intensity during the 1918 influenza pandemic
Hatchett, R. J., Mecher, C. E., & Lipsitch, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610941104 · Cited by 778
Nonpharmaceutical Interventions Implemented by US Cities During the 1918-1919 Influenza Pandemic
Markel, H., Lipman, H. B., Navarro, J. A., Sloan, A., Michalsen, J. R., Stern, A. M., & Cetron, M. S. (2007) · JAMA · doi:10.1001/jama.298.6.644 · Cited by 777
Cited by
Age-dependent effects in the transmission and control of COVID-19 epidemics
Davies, N. G., Klepac, P., Liu, Y., Prem, K., Jit, M., CMMID COVID-19 working group, Pearson, C. A. B., Quilty, B. J., Kucharski, A. J., Gibbs, H., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0962-9 · Cited by 1,759
Fong, M. W., Gao, H., Wong, J. Y., Xiao, J., Shiu, E. Y. C., Ryu, S., & Cowling, B. J. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2605.190995 · Cited by 633
“A study based on national surveillance data in France showed that routine school holidays prevented 18% of seasonal influenza cases (18%–21% in children) (64).”
Olsen, S. J., Azziz-Baumgartner, E., Budd, A. P., Brammer, L., Sullivan, S., Pineda, R. F., Cohen, C., & Fry, A. M. (2020) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6937a6 · Cited by 579
Global Influenza Seasonality: Reconciling Patterns across Temperate and Tropical Regions
Tamerius, J., Nelson, M. I., Zhou, S. Z., Viboud, C., Miller, M. A., & Alonso, W. J. (2011) · Environmental Health Perspectives · doi:10.1289/ehp.1002383 · Cited by 517
Association Between Statewide School Closure and COVID-19 Incidence and Mortality in the US
Auger, K. A., Shah, S. S., Richardson, T., Hartley, D., Hall, M., Warniment, A., Timmons, K., Bosse, D., Ferris, S. A., Brady, P. W., et al. (2020) · JAMA · doi:10.1001/jama.2020.14348 · Cited by 470
Metadata
- Authors
- Cauchemez, S., Valleron, A.-J., Boëlle, P.-Y., Flahault, A., & Ferguson, N. M.
- Venue
- Nature
- Published
- 2008-04-10
- Volume
- 452
- Issue
- 7188
- Pages
- 750-754
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2079779136
- Cited by
- 687
- References
- 23
- FWCI
- 33.94
- 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: Estimating the impact of school closure on influenza transmission from Sentinel data. https://app.cochat.ai/papers/10.1038/nature06732
- The paper
- Cauchemez, S., Valleron, A.-J., Boëlle, P.-Y., Flahault, A., & Ferguson, N. M. (2008). Estimating the impact of school closure on influenza transmission from Sentinel data. Nature. https://doi.org/10.1038/nature06732
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.