Earticle

초록

영어

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

목차

Abstract
1. 서론
2. 코로나-19 SIR 예측모형
2.1 결정론적 SIR 예측모형: 기본 모형
2.2 확률적 SIR 모형: 바이러스 변형 기간
2.3 점프 SIR 모형: 슈퍼 확산 기간
3. 코로나-19 예측모형 적용 사례
3.1 SIR 모형 예측 결과
3.2 확률적 SIR 모형 예측 결과
3.3 점프 SIR 모형 예측 결과
3.4 SIR 모형의 예측력 측정
4. 결론 및 시사점
References

키워드

저자

• 김훈 [ Hoon Kim | Associate professor, Konyang Univ. ] First Author
• 조상섭 [ Sang Sup Cho | Professor, Hoseo University, Department of Digital Technology Management ] Co Author
• 채동우 [ Dong Woo Chae | Ph. D in Economics, Hoseo University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005