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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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Detalles Bibliográficos
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
Descripción
Sumario: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.