The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
The timing of COVID-19 transmission
Open accessAbstract
The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non-pharmaceutical interventions on the spread of COVID-19 epidemics. We examined the distribution of transmission events with respect to exposure and onset of symptoms. We show that for symptomatic individuals, the timing of transmission of SARS-CoV-2 is more strongly linked to the onset of clinical symptoms of COVID-19 than to the time since infection. We found that it was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. However, we caution against overinterpretation of the right tail of the distribution, due to its dependence on behavioural factors and interventions. We also found that the pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. This strongly suggests that information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41%; 95%CI 31-50%), which limits the efficacy of symptom-based interventions, and the large fraction of transmissions (35%; 95%CI 26-45%) that occur on the same day or the day after onset of symptoms underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if they are mild. Rapid or at-home testing and contextual risk information would greatly facilitate efficient early isolation.
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“using the generation time distribution in 28 with mean 5.5 days”
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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
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Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661
Lauer, S. A., Grantz, K. H., Bi, Q., Jones, F. K., Zheng, Q., Meredith, H. R., Azman, A. S., Reich, N. G., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-0504 · Cited by 5,970
Temporal dynamics in viral shedding and transmissibility of COVID-19
He, X., Lau, E. H. Y., Wu, P., Deng, X., Wang, J., Hao, X., Lau, Y. C., Wong, J. Y., Guan, Y., Tan, X., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0869-5 · Cited by 4,300
Self-citationQuantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D., & Fraser, C. (2020) · Science · doi:10.1126/science.abb6936 · Cited by 2,512
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Oran, D. P., & Topol, E. J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-3012 · Cited by 2,348
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Arons, M. M., Hatfield, K. M., Reddy, S. C., Kimball, A. A., James, A. E., Jacobs, J. R., Taylor, J., Spicer, K. B., Bardossy, A. C., Oakley, L. P., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2008457 · Cited by 2,051
Bi, Q., Wu, Y., Mei, S., Ye, C., Zou, X., Zhang, Z., Liu, X., Wei, L., Truelove, S., Zhang, T., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30287-5 · Cited by 1,960
Pollán, M., Pérez-Gómez, B., Pastor-Barriuso, R., Oteo, J., Hernán, M. A., Pérez-Olmeda, M., Sanmartín, J. L., Fernández-García, A., Cruz, I., Fernández de Larrea, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31483-5 · Cited by 1,639
Viral load of SARS-CoV-2 in clinical samples.
Pan, Y., Zhang, D., Yang, P., Poon, L. L. M., & Wang, Q. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30113-4 · Cited by 1,562
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Metadata
- Authors
- Ferretti, L., Ledda, A., Wymant, C., Zhao, L., Ledda, V., Abeler-Dörner, L., Kendall, M., Nurtay, A., Cheng, H.-Y., Ng, T.-C., Lin, H.-H., Hinch, R., Masel, J., Kilpatrick, A. M., & Fraser, C.
- Affiliations
- Imperial College London (GB)
- University of Oxford (GB)
- Liverpool University Hospitals NHS Foundation Trust (GB)
- Taiwan Centers for Disease Control (TW)
- National Taiwan University (TW)
- University of California, Santa Cruz (US)
- Corresponding author
- Fraser, Christophe
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2020-09-07
- OpenAlex
- W3083050273
- Cited by
- 112
- References
- 52
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 17 · 2022: 30 · 2021: 44 · 2020: 9
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Funders
- Li Ka Shing Foundation
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- SARS-CoV-2, pre-symptomatic transmission, incubation period, symptom onset, infectious period, contact tracing, isolation, non-pharmaceutical interventions, rapid testing
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