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Zhu H, Chen S, Liang R, Feng Y, Joldosh A, Xie Z, Chen G, Li L, Chen K, Fang Y, Ou J. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China. BMC Infect Dis 2023; 23:299. [PMID: 37147566 PMCID: PMC10161995 DOI: 10.1186/s12879-023-08184-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/20/2023] [Indexed: 05/07/2023] Open
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.
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Affiliation(s)
- Hansong Zhu
- Fujian 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 University, Fuzhou, Fujian, 350012, China
| | - Si Chen
- Fujian Climate Center, Fuzhou, 350028, Fujian, China
| | - Rui Liang
- Department of Nutrition, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Yulin Feng
- School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Aynur Joldosh
- School of Public Health, Xiamen University, Xiamen, 361005, Fujian, China
| | - Zhonghang Xie
- Fujian 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 University, Fuzhou, Fujian, 350012, China
| | - Guangmin Chen
- Fujian 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 University, Fuzhou, Fujian, 350012, China
| | - Lingfang Li
- Fujian 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 University, Fuzhou, Fujian, 350012, China
| | - Kaizhi Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, Fujian, China.
| | - Yuanyuan Fang
- Department of Pediatric Surgery, Fujian Children's Hospital, Fuzhou, 350001, Fujian, China.
| | - Jianming Ou
- Fujian 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 University, Fuzhou, Fujian, 350012, China.
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Zhu H, Chen S, Lu W, Chen K, Feng Y, Xie Z, Zhang Z, Li L, Ou J, Chen G. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm. BMC Public Health 2022; 22:2335. [PMID: 36514013 PMCID: PMC9745690 DOI: 10.1186/s12889-022-14299-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/26/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. METHOD Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. RESULTS The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. CONCLUSION All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.
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Affiliation(s)
- Hansong Zhu
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China.
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China.
- The practice base on the school of public health Fujian Medical University, Fuzhou, 350012, Fujian, China.
| | - Si Chen
- Climate Assessment Office of Fujian Climate Center, Fuzhou, 350007, Fujian, China
| | - Wen Lu
- Shengli Clinical Medical College of Fujian Medical University, Department of Health Management of Fujian Provincial Hospital, Fuzhou, 350001, Fujian, China
| | - Kaizhi Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, Fujian, China
| | - Yulin Feng
- School of Public Health, Fujian Medical University, Fujian, 350108, Fuzhou, China
| | - Zhonghang Xie
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China
- The practice base on the school of public health Fujian Medical University, Fuzhou, 350012, Fujian, China
| | - Zhifang Zhang
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China
- Science and Technology Information and Management, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China
| | - Lingfang Li
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China
| | - Jianming Ou
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China.
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China.
- The practice base on the school of public health Fujian Medical University, Fuzhou, 350012, Fujian, China.
| | - Guangmin Chen
- Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, Fuzhou, 350012, Fujian, China.
- Fujian Provincial Key Laboratory of Zoonosis Research, Fuzhou, 350012, Fujian, China.
- The practice base on the school of public health Fujian Medical University, Fuzhou, 350012, Fujian, China.
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