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. 2022 Jun 3:10:871354.
doi: 10.3389/fpubh.2022.871354. eCollection 2022.

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

Imee V Necesito  1 , John Mark S Velasco  2   3 , Jaewon Jung  4 , Young Hye Bae  1 , Younghoon Yoo  1 , Soojun Kim  1 , Hung Soo Kim  1
Affiliations

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

Imee V Necesito et al. Front Public Health. .

Abstract

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).

Keywords: COVID-19; NARX; Niño SST index; South Korea; stringency index.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
South Korea's Weekly (A) COVID-19 cases; (B) Stringency Index (SI); and (C) Niño SST indices from January 21, 2020 to December 31, 2020 (4th week to 53rd week).
Figure 2
Figure 2
Niño SST indices regions.
Figure 3
Figure 3
Schematic diagram of the methodology.
Figure 4
Figure 4
The plot of SI and COVID-19 cases.
Figure 5
Figure 5
Plot of Niño SST Indices and COVID-19 cases.
See this image and copyright information in PMC

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References

    1. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. . A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. (2020) 382:727–33. 10.1056/NEJMoa2001017 - DOI - PMC - PubMed
    1. Santosh KC. COVID-19 prediction models and unexploited data. J Med Syst. (2020) 44:170. 10.1007/s10916-020-01645-z - DOI - PMC - PubMed
    1. Ilesanmi OS, Afolabi AA. Six months of COVID-19 response in Nigeria: lessons, challenges, and way forward. JIDH. (2020) 3:198–200. 10.47108/jidhealth.Vol3.IssSpecial1.63 - DOI
    1. Coccia M. The relation between length of lockdown, numbers of infected people and deaths of COVID-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to COVID-19 and to constrain the deterioration of economic system. Sci Total Environ. (2021). 775:145801. 10.1016/j.scitotenv.2021.145801 - DOI
    1. McBryde ES, Meehan MT, Adegboye OA, Adekunle AI, Caldwell JM, Pak A, et al. . Role of modelling in COVID-19 policy development. Paediatr Respir Rev. (2020). 35 57–60. 10.1016/j.prrv.2020.06.013 - DOI - PMC - PubMed

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