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Title: | Estimating the time interval between transmission generations when negative values occur in the serial interval data : using COVID-19 as an example | Authors: | Zhao, S | Issue Date: | 2020 | Source: | Mathematical biosciences and engineering, 2020, v. 17, no. 4, p.3512-3519 | Abstract: | The coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in the end of 2019, and soon became a serious public health threat globally. Due to the unobservability, the time interval between transmission generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be directly summarized from surveillance data. In this study, we develop a likelihood framework to estimate the TG and the pre-symptomatic transmission period from the serial interval observations from the individual transmission events. As the results, we estimate the mean of TG at 4.0 days (95%CI: 3.3−4.6), and the mean of pre-symptomatic transmission period at 2.2 days (95%CI: 1.3−4.7). We approximate the mean latent period of 3.3 days, and 32.2% (95%CI: 10.3−73.7) of the secondary infections may be due to pre-symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial for mitigating the epidemics. | Keywords: | Coronavirus disease 2019 COVID-19 Epidemic Modelling Serial interval Time of generation |
Publisher: | American Institute of Mathematical Sciences | Journal: | Mathematical biosciences and engineering | ISSN: | 1547-1063 | DOI: | 10.3934/MBE.2020198 | Rights: | © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0) The following publication Shi Zhao. Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example. Mathematical Biosciences and Engineering, 2020, 17(4): 3512-3519, is available at https://doi.org/10.3934/mbe.2020198 |
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