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Transmissibility of 1918 pandemic influenza
Open accessAbstract
The 1918 influenza pandemic killed 20-40 million people worldwide, and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case. Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases. In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required.
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“In sensitivity analyses performed to account for the interval between infection (the true measure of transmission) and death (an outcome of infection), we also considered lags of 7 or 10 days in calculating the CEPID”
From Public health interventions and epidemic intensity during the 1918 influenza pandemic (2007) · cites this paper as [12]
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Cites
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,819
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
Evolution and ecology of influenza A viruses
Webster, R. G., Bean, W. J., Gorman, O. T., Chambers, T. M., & Kawaoka, Y. (1992) · Microbiological Reviews · doi:10.1128/mr.56.1.152-179.1992 · Cited by 4,739
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
Lipsitch, M., Cohen, T., Cooper, B. S., Robins, J. M., Ma, S., James, L., Gopalakrishna, G., Chew, S. K., Tan, C. C., Samore, M. H., et al. (2003) · Science · doi:10.1126/science.1086616 · Cited by 1,485
Self-citationWallinga, J. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh255 · Cited by 1,214
Riley, S., Fraser, C., Donnelly, C. A., Ghani, A. C., Abu-Raddad, L. J., Hedley, A. J., Leung, G. M., Ho, L., Lam, T.-H., Thach, T., et al. (2003) · Science · doi:10.1126/science.1086478 · Cited by 1,152
Factors that make an infectious disease outbreak controllable
Fraser, C., Riley, S., Anderson, R. M., & Ferguson, N. M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307506101 · Cited by 1,144
Anthrax as a Biological Weapon, 2002
Inglesby, T. V., O’Toole, T., Henderson, D. A., Bartlett, J. G., Ascher, M. S., Eitzen, E. M., Friedlander, A. M., Gerberding, J. L., HAUER, J. M., Hughes, J. M., et al. (2002) · JAMA · doi:10.1001/jama.287.17.2236 · Cited by 1,023
The Sverdlovsk Anthrax Outbreak of 1979
Meselson, M. S., Guillemin, J. H., Hugh‐Jones, M., Langmuir, A. D., Popova, I., Shelokov, A., & Yampolskaya, O. V. (1994) · Science · doi:10.1126/science.7973702 · Cited by 1,005
Bioterrorism-Related Inhalational Anthrax: The First 10 Cases Reported in the United States
Jernigan, J. A., Stephens, D. S., Ashford, D. A., Omeñaca, C., Topiel, M. S., Galbraith, M., Tapper, M. L., Fisk, T. L., Zaki, S. R., Popović, T., et al. (2001) · Emerging infectious diseases · doi:10.3201/eid0706.010604 · Cited by 995
Flagged first, then by citations
Research works printed but not linked: 3
- Wolbach, S. B. Comments on the pathology and bacteriology of fatal influenza cases, as observed at Camp Devens, Mass. Johns Hopkins Hosp. Bull. 338, 104–109 (1919)
- Klotz, O. Studies on Epidemic Influenza 207–293 (Pittsburgh Univ. School of Medicine, Pittsburgh, 1919)
- Olson, D. R., Simonsen, L., Edelson, P. J. & Morse, S. S. 4th Int. Conf. on Emerging Infectious Diseases [Abstract] (CDC, Atlanta, 2004)
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Metadata
- Authors
- Mills, C. E., Robins, J. M., & Lipsitch, M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Mills, Christina E.
- Venue
- Nature
- Published
- 2004-12
- Volume
- 432
- Issue
- 7019
- Pages
- 904-906
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7095078
- OpenAlex
- W2037844075
- Cited by
- 796
- References
- 44
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 19 · 2023: 28 · 2022: 38 · 2021: 47 · 2020: 84 · 2019: 20 · 2018: 22 · 2017: 21 · 2016: 21 · 2015: 29 · 2014: 40 · 2013: 39 · 2012: 36
- FWCI
- 28.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Health and Human Services
- Ellison Medical Foundation
- National Defense Science and Engineering Graduate
- National Institute of Allergy and Infectious Diseases
- Fogarty International Center
- Topics
- Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
- Keywords
- 1918 influenza pandemic, H1N1 influenza, basic reproduction number, transmissibility, SEIR model, epidemic curve, pandemic preparedness
- MeSH terms
- Cities, Communicable Disease Control, Humans, Influenza, Human, Models, Biological, Probability, Time Factors, United States, Survival Analysis, History, 20th Century
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