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Time-variant reproductive number of COVID-19 in Seoul, Korea
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Seong-Geun Moon, Yeon-Kyung Kim, Woo-Sik Son, Jong-Hoon Kim, Jungsoon Choi, Baeg-Ju Na, Boyoung Park, Bo Youl Choi
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Epidemiol Health. 2020;42:e2020047. Published online June 28, 2020
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DOI: https://doi.org/10.4178/epih.e2020047
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 Abstract
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Abstract
OBJECTIVES
To estimate time-variant reproductive number (R<sub>t</sub>) 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 R<sub>t</sub> 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 R<sub>t</sub> based on R<sub>t_c</sub> peaked to 2.6 on February 20, 2020, then showed decreased trend and became <1.0 from March 3, 2020. Comparing both R<sub>t</sub> from R<sub>t_c</sub> and from the number of daily onset cases, we found that the pattern of changes was similar, although the variation of R<sub>t</sub> was greater when using R<sub>t_c</sub>. When we changed assumed onset date for asymptotic cases (-2 days to +2 days of the confirmed date), the results were comparable.
CONCLUSIONS R<sub>t</sub> can be estimated based on R<sub>t_c</sub> which is available from daily report of the Korea Centers for Disease Control and Prevention. Estimation of R<sub>t</sub> would be useful to continuously monitor the effectiveness of the quarantine policy at the city and province levels.
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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.
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Citations
Citations to this article as recorded by  - Reproduction Factor Based Latent Epidemic Model Inference: A Data-Driven Approach Using COVID-19 Datasets
Sujin Ahn, Minhae Kwon
IEEE Journal of Biomedical and Health Informatics.2023; 27(3): 1259. CrossRef - 코로나19 핵심 지표 산출체계 국제 비교 및 활용도 제고 방안 연구
나애 이, 연경 김, 승필 정, 우주 이, 주환 오, 승식 황
Public Health Weekly Report.2023; 16(29): 973. CrossRef - The Impacts of Compact City Characteristics on COVID-19 Spreading Force : Focused on the Seoul Metropolitan Area
Haejun Hyun, Myungje Woo
Journal of Korea Planning Association.2023; 58(7): 5. CrossRef - COVID-19 early-alert signals using human behavior alternative data
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Social Network Analysis and Mining.2021;[Epub] CrossRef
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