The prediction of influenza-like illness using national influenza surveillance data and Baidu query data
Abstract Background Seasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. Howe...
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| Format: | Article |
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BMC
2024-02-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-024-17978-0 |
| _version_ | 1797272869298765824 |
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| author | Su wei Sun Lin Zhao wenjing Song Shaoxia Yang Yuejie He Yujie Zhang Shu Li Zhong Liu Ti |
| author_facet | Su wei Sun Lin Zhao wenjing Song Shaoxia Yang Yuejie He Yujie Zhang Shu Li Zhong Liu Ti |
| author_sort | Su wei |
| collection | DOAJ |
| description | Abstract Background Seasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate. Methods The official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Results In total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China. Conclusions The results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics. |
| first_indexed | 2024-03-07T14:35:29Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-03-07T14:35:29Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-01c2f51464ae4ca297ccce36fa64d8682024-03-05T20:38:44ZengBMCBMC Public Health1471-24582024-02-0124111210.1186/s12889-024-17978-0The prediction of influenza-like illness using national influenza surveillance data and Baidu query dataSu wei0Sun Lin1Zhao wenjing2Song Shaoxia3Yang Yuejie4He Yujie5Zhang Shu6Li Zhong7Liu Ti8School of Management Science and Engineering, Shandong University of Finance and EconomicsShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineDezhou Center for Disease Control and PreventionShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineChina Institute of Water Resources and Hydropower ResearchShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineShandong Center for Disease Control and Prevention, Shandong Provincial Key Laboratory of Infectious Disease Control and Prevention, Shandong University Institution for Prevention MedicineAbstract Background Seasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate. Methods The official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Results In total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China. Conclusions The results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.https://doi.org/10.1186/s12889-024-17978-0InfluenzaForecastLSTMBaidu search index |
| spellingShingle | Su wei Sun Lin Zhao wenjing Song Shaoxia Yang Yuejie He Yujie Zhang Shu Li Zhong Liu Ti The prediction of influenza-like illness using national influenza surveillance data and Baidu query data BMC Public Health Influenza Forecast LSTM Baidu search index |
| title | The prediction of influenza-like illness using national influenza surveillance data and Baidu query data |
| title_full | The prediction of influenza-like illness using national influenza surveillance data and Baidu query data |
| title_fullStr | The prediction of influenza-like illness using national influenza surveillance data and Baidu query data |
| title_full_unstemmed | The prediction of influenza-like illness using national influenza surveillance data and Baidu query data |
| title_short | The prediction of influenza-like illness using national influenza surveillance data and Baidu query data |
| title_sort | prediction of influenza like illness using national influenza surveillance data and baidu query data |
| topic | Influenza Forecast LSTM Baidu search index |
| url | https://doi.org/10.1186/s12889-024-17978-0 |
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