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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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References

1. A. R. Tuite, D. N. Fisman, Reporting, Epidemic Growth, and Reproduction Numbers for the 2019 Novel Coronavirus (2019-nCoV) Epidemic, Ann. Intern. Med., (2020).

2. S. Zhao, P. Cao, D. Gao, Z. Zhuang, Y. Cai, J. Ran, et al., Serial interval in determining the estimation of reproduction number of the novel coronavirus disease (COVID-19) during the early outbreak, J. Travel Med., (2020), taaa033.

3. J. Riou, C. L. Althaus, Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020, Euro. Surveill., 25 (2020), 2000058.

4. P. E. M. Fine, The Interval between Successive Cases of an Infectious Disease, Am. J. Epidemiol., 158 (2003), 1039-1047.

5. L. F. White, J. Wallinga, L. Finelli, C. Reed, S. Riley, M. Lipsitch, et al., Estimation of the reproductive number and the serial interval in early phase of the 2009 influenza A/H1N1 pandemic in the USA, Influenza Other Respir. Viruses, 3 (2009), 267-276.

6. R. Milwid, A. Steriu, J. Arino, J. Heffernan, A. Hyder, D. Schanzer, et al., Toward Standardizing a Lexicon of Infectious Disease Modeling Terms, Front. Public Health, 4 (2016), 213.

7. M. A. Vink, M. C. J. Bootsma, J. Wallinga, Serial Intervals of Respiratory Infectious Diseases: A Systematic Review and Analysis, Am. J. Epidemiol., 180 (2014), 865-875.

8. J. Wallinga, M. Lipsitch, How generation intervals shape the relationship between growth rates and reproductive numbers, P. Roy. Soc. B, 274 (2007), 599-604.

9. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497-506.

10. Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia, New Engl. J. Med., 382 (2020), 1199-1207.

11. S. Zhao, S. S. Musa, Q. Lin, J. Ran, G. Yang, W. Wang, et al., Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak, J. Clin. Med., 9 (2020), 388.

12. J. T. Wu, K. Leung, G. M. Leung, Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study, Lancet, 395 (2020), 689-697.

13. S. Zhao, Z. Zhuang, P. Cao, J. Ran, D. Gao, Y. Lou, et al., Quantifying the association between domestic travel and the exportation of novel coronavirus (2019-nCoV) cases from Wuhan, China in 2020: a correlational analysis, J. Travel Med., 27 (2020), taaa022.

14. World Health Organization, Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV), 2020.

15. World Health Organization, Novel Coronavirus (2019-nCoV) situation reports, 3. 2020.

16. Z. Du, X. Xu, Y. Wu, L. Wang, B. J. Cowling, L. A. Meryers, Serial Interval of COVID-19 among Publicly Reported Confirmed Cases, Emerg. Infecti. Dis., 26 (2020).

17. S. Ma, J. Zhang, M. Zeng, Q. Yun, W. Guo, Y. Zheng, et al., Epidemiological parameters of coronavirus disease 2019: a pooled analysis of publicly reported individual data of 1155 cases from seven countries, medRxiv (2020), 2020.03.21.20040329.

18. C. You, Y. Deng, W. Hu, J. Sun, Q. Lin, F. Zhou, et al., Estimation of the Time-Varying Reproduction Number of COVID-19 Outbreak in China, medRxiv (2020), 2020.02.08.20021253.

19. B. J. Cowling, V. J. Fang, S. Riley, J. M. Peiris, G. M. Leung, Estimation of the serial interval of influenza, Epidemiol., 20 (2009), 344.

20. H. Nishiura, N. M. Linton, A. R. Akhmetzhanov, Serial interval of novel coronavirus (COVID-19) infections, Int. J. Infect. Dis., 93 (2020), 284-286.

21. S. Zhao, D. Gao, Z. Zhuang, M. Chong, Y. Cai, J. Ran, et al., Estimating the serial interval of the novel coronavirus disease (COVID-19): A statistical analysis using the public data in Hong Kong from January 16 to February 15, 2020, medRxiv (2020), 2002.02.21.20026559.

22. J. Fan, T. Huang, Profile likelihood inferences on semiparametric varying-coefficient partially linear models, Bernoulli, 11 (2005), 1031-1057.

23. S. Zhao, Q. Lin, J. Ran, S. S. Musa, G. Yang, W. Wang, et al., Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak, Int. J. Infect. Dis., 92 (2020), 214-217.

24. J. A. Backer, D. Klinkenberg, J. Wallinga, Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020, Euro. Surveill., 25 (2020), 2000062.

25. W. J. Guan, Z. Y. Ni, Y. Hu, W. H. Liang, C. Q. Ou, J. X. He, et al., Clinical Characteristics of Coronavirus Disease 2019 in China, N. Engl. J. Med., 382 (2020), 1708-1720.

26. S. A. Lauer, K. H. Grantz, Q. Bi, F. K. Jones, Q. Zheng, H. R. Meredith, et al., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application, Ann. Intern. Med., (2020).

27. L. Ferretti, C. Wymant, M. Kendall, L. Zhao, A. Nurtay, L. Abeler-Dorner, et al., Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing, Science, 368 (2020), eabb6936.

28. X. He, E. H. Y. Lau, P. Wu, X. Deng, J. Wang, X. Hao, et al., Temporal dynamics in viral shedding and transmissibility of COVID-19, Nat. Med., (2020), 1-4.

29. Y. Liu, A. A. Gayle, A. Wilder-Smith, J. Rocklov, The reproductive number of COVID-19 is higher compared to SARS coronavirus, J. Travel. Med., 27 (2020), taaa021.

30. R. Li, S. Pei, B. Chen, Y. Song, T. Zhang, W. Yang, et al., Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2), Science, 368 (2020), 489-493.

31. Z. Zhuang, S. Zhao, Q. Lin, P. Cao, Y. Lou, L. Yang, et al., Preliminary estimation of the novel coronavirus disease (COVID-19) cases in Iran: A modelling analysis based on overseas cases and air travel data, Int. J. Infect. Dis., 94 (2020), 29-31.

© 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)

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