Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China
Abstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence...
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-023-08184-1 |
| _version_ | 1797832200942518272 |
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| author | Hansong Zhu Si Chen Rui Liang Yulin Feng Aynur Joldosh Zhonghang Xie Guangmin Chen Lingfang Li Kaizhi Chen Yuanyuan Fang Jianming Ou |
| author_facet | Hansong Zhu Si Chen Rui Liang Yulin Feng Aynur Joldosh Zhonghang Xie Guangmin Chen Lingfang Li Kaizhi Chen Yuanyuan Fang Jianming Ou |
| author_sort | Hansong Zhu |
| collection | DOAJ |
| description | Abstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion This study’s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data. |
| first_indexed | 2024-04-09T14:04:00Z |
| format | Article |
| id | doaj.art-0ec5d606853e429fb7050ff64fd0ffbd |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-04-09T14:04:00Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-0ec5d606853e429fb7050ff64fd0ffbd2023-05-07T11:07:00ZengBMCBMC Infectious Diseases1471-23342023-05-0123111610.1186/s12879-023-08184-1Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, ChinaHansong Zhu0Si Chen1Rui Liang2Yulin Feng3Aynur Joldosh4Zhonghang Xie5Guangmin Chen6Lingfang Li7Kaizhi Chen8Yuanyuan Fang9Jianming Ou10Fujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical UniversityFujian Climate CenterDepartment of Nutrition, The First Affiliated Hospital of Zhengzhou UniversitySchool of Public Health, Fujian Medical UniversitySchool of Public Health, Xiamen UniversityFujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical UniversityFujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical UniversityFujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical UniversityCollege of Computer and Data Science, Fuzhou UniversityDepartment of Pediatric Surgery, Fujian Children’s HospitalFujian Provincial Center for Disease Control and Prevention, Fujian Provincial Key Laboratory of Zoonosis Research, The Practice Base On the School of Public Health Fujian Medical UniversityAbstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion This study’s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.https://doi.org/10.1186/s12879-023-08184-1MeteorologicalRelative humidityAir temperatureHFMDLSTMDLNM |
| spellingShingle | Hansong Zhu Si Chen Rui Liang Yulin Feng Aynur Joldosh Zhonghang Xie Guangmin Chen Lingfang Li Kaizhi Chen Yuanyuan Fang Jianming Ou Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China BMC Infectious Diseases Meteorological Relative humidity Air temperature HFMD LSTM DLNM |
| title | Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China |
| title_full | Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China |
| title_fullStr | Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China |
| title_full_unstemmed | Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China |
| title_short | Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China |
| title_sort | study of the influence of meteorological factors on hfmd and prediction based on the lstm algorithm in fuzhou china |
| topic | Meteorological Relative humidity Air temperature HFMD LSTM DLNM |
| url | https://doi.org/10.1186/s12879-023-08184-1 |
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