The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures
Open accessAbstract
Severe acute respiratory syndrome (SARS) has been the first severe contagious disease to emerge in the 21st century. The available epidemic curves for SARS show marked differences between the affected regions with respect to the total number of cases and epidemic duration, even for those regions in which outbreaks started almost simultaneously and similar control measures were implemented at the same time. The authors developed a likelihood-based estimation procedure that infers the temporal pattern of effective reproduction numbers from an observed epidemic curve. Precise estimates for the effective reproduction numbers were obtained by applying this estimation procedure to available data for SARS outbreaks that occurred in Hong Kong, Vietnam, Singapore, and Canada in 2003. The effective reproduction numbers revealed that epidemics in the various affected regions were characterized by markedly similar disease transmission potentials and similar levels of effectiveness of control measures. In controlling SARS outbreaks, timely alerts have been essential: Delaying the institution of control measures by 1 week would have nearly tripled the epidemic size and would have increased the expected epidemic duration by 4 weeks.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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“Estimates of important epidemiologic measures such as the case-fatality rate and the incubation period were reported in May 2003 (6) (7) (8) .”
Background
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
“Preparing for outbreaks of directly transmitted pathogens such as pandemic influenza [1] [2] [3] and SARS [4] [5] [6] [7] [8] [9] , and controlling endemic diseases such as tuberculosis and meningococcal diseases, are major public health priorities.”
BackgroundSelf-citationProjecting 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
Superspreading and the effect of individual variation on disease emergence
Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005) · Nature · doi:10.1038/nature04153 · Cited by 2,514
HCA Lung Biological Network, Sungnak, W., Huang, N., Bécavin, C., Berg, M., Queen, R., Litvinukova, M., Talavera-López, C., Maatz, H., Reichart, D., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0868-6 · Cited by 2,501
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt133 · Cited by 1,530
“(See Wallinga and Teunis (13), and Cauchemez et al. (14, 15) for methods used to estimate the case reproduction number).”
MethodResultInfluential
Metadata
- Authors
- Wallinga, J.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-09-15
- Volume
- 160
- Issue
- 6
- Pages
- 509-516
- ISSN
- 0002-9262 (ISSN-L)
- OpenAlex
- W2102187991
- Cited by
- 1,197
- References
- 2
- FWCI
- 29.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.