Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices

Frontiers in Public Health

article

Imee V. Necesito¹  John Mark S. Velasco²  Jaewon Jung⁴  Young Hye Bae¹  Younghoon Yoo¹  Soojun Kim¹  Hung Soo Kim¹ 

[1] Department of Civil Engineering, Inha University, Incheon;Department of Clinical Epidemiology, College of Medicine, University of the Philippines;Institute of Molecular Biology and Biotechnology, National Institutes of Health, University of the Philippines;Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology

关键词: COVID-19;    stringency index;    Niño SST index;    NARX;    South Korea;   

DOI  :  10.3389/fpubh.2022.871354

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

【 授权许可】

CC BY