The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China
Open accessPublished version of a preprint
Abstract
Containment works: National governments have taken different approaches in response to the coronavirus disease 2019 (COVID-19) pandemic, ranging from draconian quarantines to laissez-faire mitigation strategies. In data from China collected in February 2020, Maier and Brockmann noticed that, unexpectedly, the epidemic did not take off exponentially. Nonexponential spread occurs when the supply of susceptible individuals is depleted on a time scale comparable to the infectious period of the virus. The results of the authors' modeling approach indicate that the public response to the epidemic plus containment policies were becoming effective despite the initial increase in confirmed cases. Science , this issue p. 742
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Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S. M., Lau, E. H. Y., Wong, J. Y., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001316 · Cited by 16,575
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2
Sanche, S., Lin, Y. T., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2607.200282 · Cited by 1,619
Research works printed but not linked: 14
- 10.1126/science.abb2161 · doi:10.1126/science.abb2161
- J. Hsu, Here’s how computer models simulate the future spread of new coronavirus. Sci. Am. (13 February 2020).
- T. Lewis, China’s citywide quarantines: Are they ethical and effective? Sci. Am. (25 January 2020).
- 10.1016/j.ijid.2020.01.050 · DOI · Paper page
- 10.1371/journal.pone.0017823 · doi:10.1371/journal.pone.0017823
- 10.1002/cplx.21615 · doi:10.1002/cplx.21615
- R. M. Anderson R. M. May Infectious Diseases of Humans: Dynamics and Control Oxford Science Publications (Oxford Univ. Press 1991). · DOI · Paper page
- B. Prasse M. A. Achterberg L. Ma P. Van Mieghem Network-Based Prediction of the 2019-nCoV Epidemic Outbreak in the Chinese Province Hubei; arXiv:2002.04482 [physics q-bio] (2020). · DOI
- S. Sanche et al . The Novel Coronavirus 2019-nCoV is Highly Contagious and More Infectious Than Initially Estimated; arXiv:2002.03268 [q-bio] (2020). · DOI
- 10.1080/00036811.2020.1732357 · doi:10.1080/00036811.2020.1732357
- M. J. Keeling P. Rohani Modeling Infectious Diseases in Humans and Animals (Princeton Univ. Press 2008). · DOI · Paper page
- W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics—I. 1927. Bull. Math. Biol. 53, 33–55 (1991). 2059741 · DOI · Paper page
- A. J. Kucharski et al. Early dynamics of transmission and control of COVID-19: A mathematical modelling study. medRxiv p. 2020.01.31.20019901 (2020); https://doi.org/10.1101/2020.01.31.20019901. · doi:10.1101/2020.01.31.20019901
- J. M. Read J. R. Bridgen D. A. Cummings A. Ho C. P. Jewell Novel coronavirus 2019-nCoV: Early estimation of epidemiological parameters and epidemic predictions. medRxiv p. 2020.01.23.20018549 (2020); https://doi.org/10.1101/2020.01.23.20018549.10.1101/2020.01.23.20018549 · DOI
Cited by
The effect of large-scale anti-contagion policies on the COVID-19 pandemic
Hsiang, S., Allen, D., Annan-Phan, S., Bell, K., Bolliger, I., Chong, T., Druckenmiller, H., Huang, L. Y., Hultgren, A., Krasovich, E., et al. (2020) · Nature · doi:10.1038/s41586-020-2404-8 · Cited by 1,379
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020) · Science · doi:10.1126/science.abb9789 · Cited by 819
Farooq, A., Laato, S., & Islam, A. K. M. N. (2020) · Journal of Medical Internet Research · doi:10.2196/19128 · Cited by 467
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368
Differential effects of intervention timing on COVID-19 spread in the United States
(2020) · doi:10.1126/sciadv.abd6370 · Cited by 366
Metadata
- Authors
- Maier, B. F., & Brockmann, D.
- Venue
- Science
- Published
- 2020-05-15
- Volume
- 368
- Issue
- 6492
- Pages
- 742-746
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3014866318
- Cited by
- 898
- References
- 4
- FWCI
- 31.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- Joachim Herz Stiftung
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