A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-023-08864-y |
| _version_ | 1797388464095756288 |
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| author | Yiran Wan Ping Song Jiangchen Liu Ximing Xu Xun Lei |
| author_facet | Yiran Wan Ping Song Jiangchen Liu Ximing Xu Xun Lei |
| author_sort | Yiran Wan |
| collection | DOAJ |
| description | 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 The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. Results Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 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. |
| first_indexed | 2024-03-08T22:41:11Z |
| format | Article |
| id | doaj.art-41e64032dbda4d789aa5925aa9a691fd |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-03-08T22:41:11Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-41e64032dbda4d789aa5925aa9a691fd2023-12-17T12:08:08ZengBMCBMC Infectious Diseases1471-23342023-12-012311910.1186/s12879-023-08864-yA hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTMYiran Wan0Ping Song1Jiangchen Liu2Ximing Xu3Xun Lei4School of Public Health, Chongqing Medical UniversityBig Data Center for Children’s Medical Care, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and DisordersSchool of Mathematical Science, Chongqing Normal UniversityBig Data Center for Children’s Medical Care, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and DisordersSchool of Public Health, Chongqing Medical UniversityAbstract 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 The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. Results Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 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.https://doi.org/10.1186/s12879-023-08864-yEEMDLSTMHybrid modelHFMD prediction |
| spellingShingle | Yiran Wan Ping Song Jiangchen Liu Ximing Xu Xun Lei A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM BMC Infectious Diseases EEMD LSTM Hybrid model HFMD prediction |
| title | A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM |
| title_full | A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM |
| title_fullStr | A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM |
| title_full_unstemmed | A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM |
| title_short | A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM |
| title_sort | hybrid model for hand foot mouth disease prediction based on arima eemd lstm |
| topic | EEMD LSTM Hybrid model HFMD prediction |
| url | https://doi.org/10.1186/s12879-023-08864-y |
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