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Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020
INTRODUCTION: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: The compartment model and the ARIMA model were...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906044/ https://www.ncbi.nlm.nih.gov/pubmed/36779172 http://dx.doi.org/10.46234/ccdcw2022.239 |
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author | Qi, Bangguo Liu, Nankun Yu, Shicheng Tan, Feng |
author_facet | Qi, Bangguo Liu, Nankun Yu, Shicheng Tan, Feng |
author_sort | Qi, Bangguo |
collection | PubMed |
description | INTRODUCTION: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R(2)). RESULTS: The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R(2) values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R(2) values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively. DISCUSSION: The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model. |
format | Online Article Text |
id | pubmed-9906044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention |
record_format | MEDLINE/PubMed |
spelling | pubmed-99060442023-02-10 Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 Qi, Bangguo Liu, Nankun Yu, Shicheng Tan, Feng China CDC Wkly Methods and Applications INTRODUCTION: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. METHODS: The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R(2)). RESULTS: The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R(2) values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R(2) values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively. DISCUSSION: The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model. Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022-12-30 /pmc/articles/PMC9906044/ /pubmed/36779172 http://dx.doi.org/10.46234/ccdcw2022.239 Text en Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022 https://creativecommons.org/licenses/by-nc-sa/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ (https://creativecommons.org/licenses/by-nc-sa/4.0/) |
spellingShingle | Methods and Applications Qi, Bangguo Liu, Nankun Yu, Shicheng Tan, Feng Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title | Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title_full | Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title_fullStr | Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title_full_unstemmed | Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title_short | Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model — China, December 2019–April 2020 |
title_sort | comparing covid-19 case prediction between arima model and compartment model — china, december 2019–april 2020 |
topic | Methods and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906044/ https://www.ncbi.nlm.nih.gov/pubmed/36779172 http://dx.doi.org/10.46234/ccdcw2022.239 |
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