(영문) 국내 코로나 감염 네트워크 분석 (Analysis of COVID-19 Transmission Network in Korea)

소개

- 2020년 1월 20일 ~ 2020년 6월1일 동안의 국내 코로나 감염 데이터를 이용해 감염 네트워크를 Graph Theory를 이용해 분석했습니다
- 레포트와 PPT 둘 다 있습니다!!
- 성적: A+ (레포트 파일에 인증샷 포함)

목차

1. Introduction
2. Literature review
3. Method
4. Application
5. Result and Discussion
6. Conclusion

본문내용

In this study, a COVID-19 transmission network is analyzed to find patterns of transmission dynamics from super-spreaders. This is important because preventing super-spreaders is the most efficient way of implementing quarantine policies. As a result of the analysis, it turned out that the top 1% of super-spreaders account for 12% of the total infections.
The data is collected by KDCA, containing information about 4004 Korean patients who are infected with coronavirus in Korea, from January 20th 2020 to June 1st 2020.
The main method used in this study is graph theory. This is because we wanted to find extraordinary infection cases and super-spreaders, with their demographic information. The transmission network turned out to be a scale-free network, showing characteristics of small-world network and hub nodes. Based on meaningful transmission dynamics found, significant infection cases were identified and their patterns were extracted for a proposal to current quarantine policies.

참고자료

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