Key result
The long short-term memory (LSTM) deep learning model demonstrated superior forecasting performance for hand-foot-mouth disease incidence compared to SARIMA and NAR models.
Why the study?
The high incidence, seasonal pattern, and frequent outbreaks of hand-foot-mouth disease pose a threat to children in mainland China, requiring advanced response approaches.
The LSTM deep learning approach outperformed traditional SARIMA and NAR models in forecasting the incidence and seasonal trends of hand-foot-mouth disease in mainland China.
LSTM forecasting may aid HFMD surveillance; leaves open prospective validation against ARIMA.
The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.
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Wang et al. (2019) studied Hand-foot-mouth disease (n=19,218,824). Long short-term memory (LSTM) model vs. SARIMA and NAR models was evaluated on Forecasting performance (RMSE, MAE, MAPE). The long short-term memory (LSTM) deep learning model demonstrated superior forecasting performance for hand-foot-mouth disease incidence compared to SARIMA and NAR models.
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