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Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example

1 Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
2 School of Nursing, Hong Kong Polytechnic University, Hong Kong, China
3 JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China
4 CUHK Shenzhen Research Institute, Shenzhen, China

Special Issues: Modeling the Biological, Epidemiological, Immunological, Molecular, Virological Aspects of COVID-19

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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Keywords coronavirus disease 2019; COVID-19; time of generation; serial interval; epidemic; modelling

Citation: 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. doi: 10.3934/mbe.2020198


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