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Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices
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,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204014/ https://www.ncbi.nlm.nih.gov/pubmed/35719622 http://dx.doi.org/10.3389/fpubh.2022.871354 |
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author | Necesito, Imee V. Velasco, John Mark S. Jung, Jaewon Bae, Young Hye Yoo, Younghoon Kim, Soojun Kim, Hung Soo |
author_facet | Necesito, Imee V. Velasco, John Mark S. Jung, Jaewon Bae, Young Hye Yoo, Younghoon Kim, Soojun Kim, Hung Soo |
author_sort | Necesito, Imee V. |
collection | PubMed |
description | 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). |
format | Online Article Text |
id | pubmed-9204014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92040142022-06-18 Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices Necesito, Imee V. Velasco, John Mark S. Jung, Jaewon Bae, Young Hye Yoo, Younghoon Kim, Soojun Kim, Hung Soo Front Public Health Public Health 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). Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9204014/ /pubmed/35719622 http://dx.doi.org/10.3389/fpubh.2022.871354 Text en Copyright © 2022 Necesito, Velasco, Jung, Bae, Yoo, Kim and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Necesito, Imee V. Velasco, John Mark S. Jung, Jaewon Bae, Young Hye Yoo, Younghoon Kim, Soojun Kim, Hung Soo Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title | Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title_full | Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title_fullStr | Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title_full_unstemmed | Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title_short | Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices |
title_sort | predicting covid-19 cases in south korea using stringency and niño sea surface temperature indices |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204014/ https://www.ncbi.nlm.nih.gov/pubmed/35719622 http://dx.doi.org/10.3389/fpubh.2022.871354 |
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