The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Mitigation strategies for pandemic influenza in the United States
Abstract
Recent human deaths due to infection by highly pathogenic (H5N1) avian influenza A virus have raised the specter of a devastating pandemic like that of 1917–1918, should this avian virus evolve to become readily transmissible among humans. We introduce and use a large-scale stochastic simulation model to investigate the spread of a pandemic strain of influenza virus through the U.S. population of 281 million individuals forR0(the basic reproductive number) from 1.6 to 2.4. We model the impact that a variety of levels and combinations of influenza antiviral agents, vaccines, and modified social mobility (including school closure and travel restrictions) have on the timing and magnitude of this spread. Our simulations demonstrate that, in a highly mobile population, restricting travel after an outbreak is detected is likely to delay slightly the time course of the outbreak without impacting the eventual number ill. ForR0< 1.9, our model suggests that the rapid production and distribution of vaccines, even if poorly matched to circulating strains, could significantly slow disease spread and limit the number ill to <10% of the population, particularly if children are preferentially vaccinated. Alternatively, the aggressive deployment of several million courses of influenza antiviral agents in a targeted prophylaxis strategy may contain a nascent outbreak with lowR0, provided adequate contact tracing and distribution capacities exist. For higherR0, we predict that multiple strategies in combination (involving both social and medical interventions) will be required to achieve similar limits on illness rates.
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
Characterization of the reconstructed 1918 Spanish influenza pandemic virus
Tumpey, T. M., Basler, C. F., Aguilar, P. V., Zeng, H., Solórzano, A., Swayne, D. E., Cox, N. J., Katz, J. M., Taubenberger, J. K., Palese, P., et al. (2005) · Science · doi:10.1126/science.1119392 · Cited by 1,327
Transmissibility of 1918 pandemic influenza
Mills, C. E., Robins, J. M., & Lipsitch, M. (2004) · Nature · doi:10.1038/nature03063 · Cited by 796
Cited by
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050074 · Cited by 2,900
“For instance, household size, class size, transport statistics, and workplace size distribution have been used in recent models to define the contact structure [2,3,33,34].”
MethodPartisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic
Gadarian, S. K., Goodman, S. W., & Pepinsky, T. B. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249596 · Cited by 744
Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic
Bajardi, P., Poletto, C., Ramasco, J. J., Tizzoni, M., Colizza, V., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016591 · Cited by 495
Antiviral Resistance and the Control of Pandemic Influenza
Lipsitch, M., Cohen, T., Murray, M., & Levin, B. R. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040015 · Cited by 209
“Prior studies [3–5, 11 ] and our Successful nondrug interventions, which themselves reduce the attack rate, considerably amplify the benefits of antiviral use, even in the presence of resistance (Figure 4, blue and green curves compared to purple and black curves).”
BackgroundInfluentialFormalizing the role of agent-based modeling in causal inference and epidemiology
Marshall, B. D. L., & Galea, S. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu274 · Cited by 192
Metadata
- Authors
- Germann, T. C., Kadau, K., Longini, I. M., & Macken, C. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2006-04-11
- Volume
- 103
- Issue
- 15
- Pages
- 5935-5940
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2142240784
- Cited by
- 1,047
- References
- 2
- FWCI
- 60.87
- Type
- journal article
- Language
- en
- Open access
- closed
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: Mitigation strategies for pandemic influenza in the United States. https://app.cochat.ai/papers/10.1073/pnas.0601266103
- The paper
- Germann, T. C., Kadau, K., Longini, I. M., & Macken, C. A. (2006). Mitigation strategies for pandemic influenza in the United States. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0601266103
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.