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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...

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Autores principales: Qi, Bangguo, Liu, Nankun, Yu, Shicheng, Tan, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2022
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.
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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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