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Commentary on Ferguson, et al., “Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand”
Open accessAbstract
A recent manuscript (Ferguson et al. in Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team, London, 2020. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf) from Imperial College modelers examining ways to mitigate and control the spread of COVID-19 has attracted much attention. In this paper, we will discuss a coarse taxonomy of models and explore the context and significance of the Imperial College and other models in contributing to the analysis of COVID-19.
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Citations
Cites
Public 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
Research works printed but not linked: 16
- Abbey H (1952) An examination of the Reed–Frost theory of epidemics. Hum Biol 24(3):201
- Ball F (1983) A threshold theorem for the Reed–Frost chain-binomial epidemic. J Appl Probab 20(1):153–157 · doi:10.2307/3213729
- Black FL, Singer B (1987) Elaboration versus simplification in refining mathematical models of infectious disease. Annu Rev Microbiol 41(1):677–701 · doi:10.1146/annurev.mi.41.100187.003333
- Eubank S, Guclu H, Kumar VA, Marathe MV, Srinivasan A, Toroczkai Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429(6988):180–184 · DOI · Paper page
- Ferguson NM, Keeling MJ, Edmunds WJ, Gani R, Grenfell BT, Anderson RM, Leach S (2003) Planning for smallpox outbreaks. Nature 425(6959):681–685 · DOI · Paper page
- Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437(7056):209–214 · DOI · Paper page
- Gani R, Leach S (2001) Transmission potential of smallpox in contemporary populations. Nature 414(6865):748–751 · DOI · Paper page
- Halloran ME, Longini IM, Nizam A, Yang Y (2002) Containing bioterrorist smallpox. Science 298(5597):1428–1432 · DOI · Paper page
- Halloran ME, Ferguson NM, Eubank S, Longini IM, Cummings DA, Lewis B, Xu S, Fraser C, Vullikanti A, Germann TC, Wagener D (2008) Modeling targeted layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci 105(12):4639–4644 · DOI · Paper page
- Hethcote HW (1994) A thousand and one epidemic models. In: Levin SA (ed) Frontiers in mathematical biology. Springer, Berlin, pp 504–515 · DOI
- Kaplan EH, Craft DL, Wein LM (2002) Emergency response to a smallpox attack: the case for mass vaccination. Proc Natl Acad Sci 99(16):10935–10940 · DOI · Paper page
- Lofgren ET, Halloran ME, Rivers CM, Drake JM, Porco TC, Lewis B, Yang W, Vespignani A, Shaman J, Eisenberg JN, Eisenberg MC (2014) Opinion: mathematical models: a key tool for outbreak response. Proc Natl Acad Sci 111(51):18095–18096 · DOI
- Longini IM, Nizam A, Xu S, Ungchusak K, Hanshaoworakul W, Cummings DA, Halloran ME (2005) Containing pandemic influenza at the source. Science 309(5737):1083–1087 · DOI · Paper page
- Markel H, Stern AM, Cetron MS, Theodore E (2008) Woodward award: non-pharmaceutical interventions employed by major American cities during the 1918–19 influenza pandemic. Trans Am Clin Climatol Assoc 119:129–138 (discussion 138–142)
- medRxiv, the preprint server for health sciences (2020) COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv. https://connect.medrxiv.org/relate/content/181
- Morris M, Kretzschmar M (1997) Concurrent partnerships and the spread of HIV. Aids 11(5):641–648 · DOI · Paper page
Cited by
Viner, R. M., Russell, S. J., Croker, H., Packer, J., Ward, J., Stansfield, C., Mytton, O., Bonell, C., & Booy, R. (2020) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(20)30095-x · Cited by 2,213
Roberton, T., Carter, E. D., Chou, V. B., Stegmuller, A. R., Jackson, B. D., Tam, Y., Sawadogo-Lewis, T., & Walker, N. (2020) · The Lancet Global Health · doi:10.1016/s2214-109x(20)30229-1 · Cited by 1,379
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
Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures
Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2004978117 · Cited by 1,051
Davies, N. G., Kucharski, A. J., Eggo, R. M., Gimma, A., Edmunds, W. J., Jombart, T., O'Reilly, K., Endo, A., Hellewell, J., Nightingale, E. S., et al. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30133-x · Cited by 848
Metadata
- Authors
- Eubank, S., Eckstrand, I., Lewis, B., Venkatramanan, S., Marathe, M., & Barrett, C. L.
- Venue
- Bulletin of Mathematical Biology
- Published
- 2020-04
- Volume
- 82
- Issue
- 4
- Pages
- 52-52
- ISSN
- 0092-8240 (ISSN-L)
- 1522-9602
- OpenAlex
- W3015056889
- Cited by
- 881
- References
- 1
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor, 189 d)
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- CoChat. (2026). Citation check: Commentary on Ferguson, et al., “Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand”. https://app.cochat.ai/papers/10.1007/s11538-020-00726-x
- The paper
- Eubank, S., Eckstrand, I., Lewis, B., Venkatramanan, S., Marathe, M., & Barrett, C. L. (2020). Commentary on Ferguson, et al., “Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand”. Bulletin of Mathematical Biology. https://doi.org/10.1007/s11538-020-00726-x
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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.