Article ; Online: 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 : MBE
2020 Volume 17, Issue 4, Page(s) 3512–3519
Abstract: ... a serious public health threat globally. Due to the unobservability, the time interval between transmission ... symptomatic transmission. The timely and effectively isolation of symptomatic COVID-19 cases is crucial ... generations (TG), though important for understanding the disease transmission patterns, of COVID-19 cannot be ...
| 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. |
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| MeSH term(s) | Basic Reproduction Number/statistics & numerical data ; Betacoronavirus ; COVID-19 ; China/epidemiology ; Coronavirus Infections/epidemiology ; Coronavirus Infections/transmission ; Humans ; Likelihood Functions ; Mathematical Concepts ; Models, Biological ; Pandemics/statistics & numerical data ; Pneumonia, Viral/epidemiology ; Pneumonia, Viral/transmission ; SARS-CoV-2 ; Time Factors |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-09-25 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2265126-3 |
| ISSN | 1551-0018 ; 1547-1063 |
| ISSN (online) | 1551-0018 |
| ISSN | 1547-1063 |
| DOI | 10.3934/mbe.2020198 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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