ArticleBMC public health2022
Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm.
Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Associations between sunshine and influenza or influenza-like illness, a systematic review and meta-analysis.International journal of biometeorology · 2026Pooled it
- Spatial-temporal epidemiological characteristics of influenza and its correlation with meteorological factors in Gansu province based on 24 solar terms.BMC public health · 2026Article
- Spatiotemporal epidemiology of influenza in Fujian Province: an 18-year observational study.BMC infectious diseases · 2026Observational
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- Heat effects on influenza-like illness and heterogeneity factors across regions: based on 333 cities in China.BMC public health · 2026Article
- Influence of Both La Nina and Island Isolation During COVID-19 on the Epidemiology of Infectious Diseases in New Caledonia.Epidemiologia (Basel, Switzerland) · 2026Article
- Article
- LSTM-Based Recurrent Neural Network Predicts Influenza-Like-Illness in Variable Climate Zones.Immunity, inflammation and disease · 2026Article
- Hybrid neural network models for time series disease prediction confronted by spatiotemporal dependencies.MethodsX · 2025Article
- Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTM.Lancet regional health. Americas · 2025Article
- A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.BMC public health · 2025Article
- Long short-term memory-based forecasting of influenza epidemics using surveillance and meteorological data in Tokyo, Japan.Frontiers in public health · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Impact of meteorological factors on transmission of respiratory viruses across all age groups in the hot arid climate in Qatar.Frontiers in public health · 2025Article
- Study of the driving factors of the abnormal influenza A (H3N2) epidemic in 2022 and early predictions in Xiamen, China.BMC infectious diseases · 2024Article
- Article
- Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period.BMC infectious diseases · 2024Article
- Forecasting rheumatoid arthritis patient arrivals by including meteorological factors and air pollutants.Scientific reports · 2024Article
- The prediction of influenza-like illness using national influenza surveillance data and Baidu query data.BMC public health · 2024Article
- Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.BMC infectious diseases · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
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.
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