Comparison on predictive capacity of ARIMA model and LSTM model for incidence of hand, foot and mouth disease in Shijiazhuang
10.16462/j.cnki.zhjbkz.2020.01.015
- Author:
Qiu-ju GAO
1
;
Yu-chang ZHOU
;
Shu-qing ZHAO
;
Shi-yong ZHANG
Author Information
1. Department of the Preventive and Protective Medicine, NCOs of the Army Medical University, Shijiazhuang 050081, China
- Publication Type:Research Article
- Keywords:
Hand, foot and mouth disease;
ARIMA;
LSTM;
Monthly incidence;
Prediction
- From:
Chinese Journal of Disease Control & Prevention
2020;24(1):73-78
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict the incidence of hand, foot and mouth disease (HFMD) in Shijiazhuang using the multiple seasonal autoregressive integrated moving average model (ARIMA) and long short term memory (LSTM) model, lay theoretical foundation for the prevention and control of HFMD. Methods Multiple seasonal ARIMA model and LSTM model were established separately by using Eviews 8.0 and python 3.7.1 according to the data of monthly incidence of HFMD from January 2013 to May 2018 in Shijiazhuang, and the data from June 2018 to May 2019 were used to verify the prediction precision of model. Finally, the monthly incidence from June to August 2019 was predicted. Results Based on the monthly incidence from January 2013 to May 2018, the optimal models, ARIMA(1,0,0)×(1,1,2)12 and LSTM model were established. Mean absolute percentage of error (MAPE) of ARIMA and LSTM model were 22.14 and 10.03 respectively based on the monthly incidence from June to December 2018, while MAPE of ARIMA and LSTM model were 43.84 and 25.26 respectively based on the monthly incidence from June 2018 to May 2019. These results indicated that LSTM model was superior to ARIMA model in model fitting degree and predicting accuracy, which was relatively consistent with the actual situation. Conclusions LSTM model is able to fit and predict the incidence trend of HFMD well in Shijiazhuang. It can provide guidance to HFMD epidemic prediction and alerting.
