Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

BMC Public Health

Research

Si Chen¹  Kaizhi Chen²  Lingfang Li³  Hansong Zhu⁴  Guangmin Chen⁴  Zhonghang Xie⁴  Jianming Ou⁴  Zhifang Zhang⁵  Yulin Feng⁶  Wen Lu⁷ 

[1] Climate Assessment Office of Fujian Climate Center, 350007, Fuzhou, Fujian, China;College of Computer and Data Science, Fuzhou University, 350108, Fuzhou, Fujian, China;Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, 350012, Fuzhou, Fujian, China;Fujian Provincial Key Laboratory of Zoonosis Research, 350012, Fuzhou, Fujian, China;Emergency Response and Epidemic Management Institute, Fujian Center for Disease Control and Prevention, 350012, Fuzhou, Fujian, China;Fujian Provincial Key Laboratory of Zoonosis Research, 350012, Fuzhou, Fujian, China;The practice base on the school of public health Fujian Medical University, 350012, Fuzhou, Fujian, China;Fujian Provincial Key Laboratory of Zoonosis Research, 350012, Fuzhou, Fujian, China;Science and Technology Information and Management, Fujian Center for Disease Control and Prevention, 350012, Fuzhou, Fujian, China;School of Public Health, Fujian Medical University, 350108, Fuzhou, Fujian, China;Shengli Clinical Medical College of Fujian Medical University, Department of Health Management of Fujian Provincial Hospital, 350001, Fuzhou, Fujian, China;

关键词: Meteorological;    Influenza;    DLNM;    LSTM;   

DOI  :  10.1186/s12889-022-14299-y

 received in 2022-04-22, accepted in 2022-09-26,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundInfluenza 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.MethodDistributed 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.ResultsThe 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.ConclusionAll 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.

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

【 图 表 】

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