December 15, 2023Open Access

A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM

YWYiran WanNew York University PSPing SongXi'an University of Technology JLJiangchen LiuChongqing Normal University

Structured PICO

Population

Hand, foot, and mouth disease (HFMD) time series data

Intervention

ARIMA-EEMD-LSTM hybrid model

Comparator

ARIMA model, LSTM model, ARIMA-LSTM model, and EEMD-LSTM model

Outcome

Prediction accuracy of HFMD trend

A hybrid ARIMA-EEMD-LSTM model improves the prediction accuracy of hand-foot-mouth disease trends compared to traditional and non-integrated models.

Abstract

BACKGROUND: Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD. METHODS: ) were adopted to evaluate the performances of the prediction models. RESULTS: of 6.20, 3.98 and 0.996. CONCLUSION: Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.

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