ArticleBMC infectious diseases2019
Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.
Article in BMC infectious diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Probabilistic forecasting of hand, foot and mouth disease in Mainland China using Bayesian additive regression tree model.Scientific reports · 2025Article
- Characteristics and trends of unintentional injuries among children and adolescents in Kunshan, China: a hospital-based retrospective study, 2018-2023.Frontiers in public health · 2025Article
- Article
- Analytical reference framework to analyze non-COVID-19 events.Population health metrics · 2023Article
- Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China.Iranian journal of public health · 2023Article
- Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China.PLoS neglected tropical diseases · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.International journal of environmental research and public health · 2023Article
- Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.Environmental science and pollution research international · 2023Article
- Article
- The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study.BMC infectious diseases · 2022Article
- Transmissibility of hand, foot, and mouth disease in 97 counties of China.Scientific reports · 2022Article
- Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.PloS one · 2022Article
- Association between Meteorological Parameters and Hand, Foot and Mouth Disease in Mainland China: A Systematic Review and Meta-Analysis.Iranian journal of public health · 2021Review
- Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China.International journal of environmental research and public health · 2021Article
- Predictive analysis of the number of human brucellosis cases in Xinjiang, China.Scientific reports · 2021Article
- The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review.Heliyon · 2021Review
- Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.BMC infectious diseases · 2021Article
- Trend analysis and forecast of daily reported incidence of hand, foot and mouth disease in Hubei, China by Prophet model.Scientific reports · 2021Article
- Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.PloS one · 2021Article
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11 authors.
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Abstract
backgroundHand, foot and mouth disease (HFMD) is a rising public health problem and has attracted considerable attention worldwide. The purpose of this study was to develop an optimal model with meteorological factors to predict the epidemic of HFMD.
methodsTwo types of methods, back propagation neural networks (BP) and auto-regressive integrated moving average (ARIMA), were employed to develop forecasting models, based on the monthly HFMD incidences and meteorological factors during 2009-2016 in Jiangsu province, China. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed to select model and evaluate the performance of the models.
resultsFour models were constructed. The multivariate BP model was constructed using the HFMD incidences lagged from 1 to 4 months, mean temperature, rainfall and their one order lagged terms as inputs. The other BP model was fitted just using the lagged HFMD incidences as inputs. The univariate ARIMA model was specified as ARIMA (1,0,1)(1,1,0)
conclusionThe multivariate BP model effectively integrated the autocorrelation of the HFMD incidence series. Meanwhile, it also comprehensively combined the climatic variables and their hysteresis effects. The introduction of the climate terms significantly improved the prediction accuracy of the BP model. This model could be an ideal method to predict the epidemic level of HFMD, which is of great importance for the public health authorities.
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