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COVID-19: Brief Communication
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
  • 12,083 View
  • 335 Download
  • 2 Web of Science
  • 3 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
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.
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.

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
  • 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
COVID-19: Original Article
Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation
Okyu Kwon, Woo-Sik Son, Jin Yong Kim, Jong-Hun Kim
Epidemiol Health. 2020;42:e2020045.   Published online June 23, 2020
DOI: https://doi.org/10.4178/epih.e2020045
  • 11,163 View
  • 291 Download
  • 4 Web of Science
  • 3 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
Objectives
In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model.
Methods
We used the susceptible-infectious-recovery (SIR) model for a virtual population group connected by a special structured network. In the model, the infected state (<i>I</i>) was divided into <i>I</i> in which the infection is undetected and <i>I<sub>x</sub></i> in which the infection is detected. The probability of transitioning from an I state to <i>I<sub>x</sub></i> can be viewed as the rate at which an infected person is found. We assumed that only those connected to each other in the network can cause infection. In addition, this study attempted to evaluate the effects of isolation by temporarily removing the connection among these people.
Results
In Scenario 1, only the infected are isolated; in Scenario 2, those who are connected to an infected person and are also found to be infected are isolated as well. In Scenario 3, everyone connected to an infected person are isolated. In Scenario 3, it was possible to effectively suppress the infectious disease even with a relatively slow rate of diagnosis and relatively high infection rate.
Conclusions
During the epidemic, quick identification of the infected is helpful. In addition, it was possible to quantitatively show through a simulation evaluation that the management of infected individuals as well as those who are connected greatly helped to suppress the spread of infectious diseases.
Summary
Korean summary
본 연구는 행위자 기반 모형의 시뮬레이션 평가를 통해 COVID-19 유행 상황에서 비약물적 중재 효과를 정량적으로 제시하였다. 비약물적 중재에 관한 세 가지 시나리오를 통해 제시한 결과에서, COVID-19 감염자를 신속하게 진단하고, 감염자 본인과 접촉자들을 가능한 한 빨리 모두 격리하여 관리하는 것이 감염병 확산을 억제하는데 있어서 보다 효과적이었다.

Citations

Citations to this article as recorded by  
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Non-pharmaceutical interventions during the COVID-19 pandemic: A review
    Nicola Perra
    Physics Reports.2021; 913: 1.     CrossRef
  • Dissection of non-pharmaceutical interventions implemented by Iran, South Korea, and Turkey in the fight against COVID-19 pandemic
    Mohammad Keykhaei, Sogol Koolaji, Esmaeil Mohammadi, Reyhaneh Kalantar, Sahar Saeedi Moghaddam, Arya Aminorroaya, Shaghayegh Zokaei, Sina Azadnajafabad, Negar Rezaei, Erfan Ghasemi, Nazila Rezaei, Rosa Haghshenas, Yosef Farzi, Sina Rashedi, Bagher Larijan
    Journal of Diabetes & Metabolic Disorders.2021; 20(2): 1919.     CrossRef
COVID-19: Methods
Individual-based simulation model for COVID-19 transmission in Daegu, Korea
Woo-Sik Son, RISEWIDs Team
Epidemiol Health. 2020;42:e2020042.   Published online June 15, 2020
DOI: https://doi.org/10.4178/epih.e2020042
  • 11,403 View
  • 286 Download
  • 7 Web of Science
  • 5 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.
METHODS
Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.
RESULTS
Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.
CONCLUSIONS
Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.
Summary
Korean summary
신천지 교인 집단이 hotspot이 되어 지역사회로 전파된 대구의 COVID-19 확산을 시뮬레이션하였다. Individual based model을 이용하여 신천지 교인 집단, 즉 hotspot과 non-hotspot이 서로 다른 감염 확률을 갖고 있음을 확인하였으며, 4월 6일로 예정된 개학이 대구 지역 COVID-19 확산에 어떤 영향을 미칠지 분석하였다.

Citations

Citations to this article as recorded by  
  • Using simulation modelling and systems science to help contain COVID‐19: A systematic review
    Weiwei Zhang, Shiyong Liu, Nathaniel Osgood, Hongli Zhu, Ying Qian, Peng Jia
    Systems Research and Behavioral Science.2023; 40(1): 207.     CrossRef
  • Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature
    Hyojung Lee, Sol Kim, Minyoung Jeong, Eunseo Choi, Hyeonjeong Ahn, Jeehyun Lee
    Yonsei Medical Journal.2023; 64(1): 1.     CrossRef
  • Transition from growth to decay of an epidemic due to lockdown
    Hamid Khataee, Jack Kibble, Istvan Scheuring, Andras Czirok, Zoltan Neufeld
    Biophysical Journal.2021; 120(14): 2872.     CrossRef
  • A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, Italy: Model Development
    Giuseppe Giacopelli
    JMIRx Med.2021; 2(3): e24630.     CrossRef
  • Comparison of Psychosocial Distress in Areas With Different COVID-19 Prevalence in Korea
    Mina Kim, In-Hoo Park, Young-Shin Kang, Honey Kim, Min Jhon, Ju-Wan Kim, Seunghyong Ryu, Ju-Yeon Lee, Jae-Min Kim, Jonghun Lee, Sung-Wan Kim
    Frontiers in Psychiatry.2020;[Epub]     CrossRef

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