A comparative study of time series models in predicting COVID-19 cases
10.3760/cma.j.cn112338-20201116-01333
- VernacularTitle:时间序列模型应用于新型冠状病毒肺炎疫情预测效果比较研究
- Author:
Zhongqi LI
1
;
Bilin TAO
;
Mengyao ZHAN
;
Zhuchao WU
;
Jizhou WU
;
Jianming WANG
Author Information
1. 南京医科大学公共卫生学院全球健康中心流行病学系 211166
- Keywords:
COVID-19;
Autoregressive integrated moving average model;
Recurrent neural network model;
Predicting
- From:
Chinese Journal of Epidemiology
2021;42(3):421-426
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To compare the performances of different time series models in predicting COVID-19 in different countries.Methods:We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020.Results:For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively.Conclusions:The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.
