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Report 5: Phylogenetic analysis of SARS-CoV-2
Open accessAbstract
Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.
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When SARS-CoV-2 was first identified, initial assessments of its potential epidemic impact and intervention options were informed by epidemiological and modelling analyses.
“In conclusion, when SARS-CoV-2 was first identified, initial assessments of its potential epidemic impact and intervention options were informed by epidemiological and modelling analyses.”
From Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review (2025) · cites this paper as [129]
The Results report early modelling estimates of epidemic growth and cumulative infections, supporting the claim about assessing potential epidemic impact. The Results also say the exponential growth model does not account for transmission reductions from travel bans or quarantine; the paper does not assess intervention options. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe analyses, in the absence of characterisation of the new coronavirus, assumed that its natural history and epidemiology (eg, degree of superspreading) would be similar to those of SARS-CoV-1 and MERS-CoV.
“The analyses, in the absence of characterisation of the new coronavirus, assumed that its natural history and epidemiology (eg, degree of superspreading) would be similar to those of SARS-CoV-1 and MERS-CoV.25,26,129,130”
From Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review (2025) · cites this paper as [129]
The Methods section (page not available) uses a SARS-like overdispersion parameter in a transmission model, supporting the superspreading example. It also examines a scenario with no overdispersion. The report does not say it assumed SARS-CoV-2’s natural history and epidemiology generally resembled both SARS-CoV-1 and MERS-CoV; MERS-CoV is not mentioned in the relevant passages. (LLM, conf=0.86)
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Cites
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
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Morgenstern, C., Rawson, T., Routledge, I., Kont, M., Imai-Eaton, N., Skarp, J., Doohan, P., McCain, K., Johnson, R., Unwin, H. J. T., et al. (2025) · The Lancet Microbe · doi:10.1016/j.lanmic.2025.101144 · Cited by 6
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Metadata
- Authors
- Volz, E., Baguelin, M., Bhatia, S., Boonyasiri, A., Cori, A., Cucunuba Perez, Z., Cuomo-Dannenburg, G., Donnelly, C., Dorigatti, I., Fitzjohn, R., Fu, H., Gaythorpe, K., Ghani, A., Hamlet, A., Hinsley, W., Imai, N., Laydon, D., Nedjati Gilani, G., Okell, L., Riley, S., van Elsland, S., Wang, H., Wang, Y., Xi, X., & Ferguson, N.
- Affiliations
- Imperial College London (GB)
- Imperial College London
- ORCID
- Venue
- Imperial College London
- Publisher
- Imperial College London
- Published
- 2020-02-15
- OpenAlex
- W3026844949
- Cited by
- 25
- References
- 1
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 8 · 2020: 10
- Type
- report
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Imperial College Research Computing Service Data Repository
- 8
- Topics
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- SARS-CoV-2, COVID-19 pandemic, phylogenetic analysis, complete genome sequence, genetic diversity, epidemic origin, epidemic doubling time, phylodynamics, epidemic size estimation, overdispersion, reproduction number, secondary infection
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