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Jungsoon Choi 1 Article
Time-variant reproductive number of COVID-19 in Seoul, Korea
Seong-Geun Moon, Yeon-Kyung Kim, Woo-Sik Son, Jong-Hoon Kim, Jungsoon Choi, Baeg-Ju Na, Boyoung Park, Bo Youl Choi
Epidemiol Health. 2020;42:e2020047.   Published online June 28, 2020
DOI: https://doi.org/10.4178/epih.e2020047
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AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
To estimate time-variant reproductive number (Rt) of coronavirus disease 19 based on either number of daily confirmed cases or their onset date to monitor effectiveness of quarantine policies.
METHODS
Using number of daily confirmed cases from January 23, 2020 to March 22, 2020 and their symptom onset date from the official website of the Seoul Metropolitan Government and the district office, we calculated Rt using program R’s package “EpiEstim”. For asymptomatic cases, their symptom onset date was considered as -2, -1, 0, +1, and +2 days of confirmed date.
RESULTS
Based on the information of 313 confirmed cases, the epidemic curve was shaped like ‘propagated epidemic curve’. The daily Rt based on Rt_c peaked to 2.6 on February 20, 2020, then showed decreased trend and became <1.0 from March 3, 2020. Comparing both Rt from Rt_c and from the number of daily onset cases, we found that the pattern of changes was similar, although the variation of Rt was greater when using Rt_c. When we changed assumed onset date for asymptotic cases (-2 days to +2 days of the confirmed date), the results were comparable.
CONCLUSIONS
Rt can be estimated based on Rt_c which is available from daily report of the Korea Centers for Disease Control and Prevention. Estimation of Rt would be useful to continuously monitor the effectiveness of the quarantine policy at the city and province levels.
Summary
Korean summary
우리나라 전체와 각 시도별 일별 증상 발현자 수 또는 확진자 수를 이용하여 추정한 Rt로 방역정책의 효과를 국가 및 시도 수준에서 지속적으로 모니터링 할 필요가 있다.
Key Message

Citations

Citations to this article as recorded by  
  • COVID-19 early-alert signals using human behavior alternative data
    Anasse Bari, Aashish Khubchandani, Junzhang Wang, Matthias Heymann, Megan Coffee
    Social Network Analysis and Mining.2021;[Epub]     CrossRef

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