ArticleBMJ open2019
Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 42 citations in OpenAlex.
- Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh's Urban centers.BMC public health · 2025Article
- Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022.Frontiers in public health · 2023Article
- Impact of Tree Cover Loss on Carbon Emission: A Learning-Based Analysis.Computational intelligence and neuroscience · 2023Article
- Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.BMC public health · 2022Article
- Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.Scientific reports · 2022Article
- ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021.BMC public health · 2022Article
- Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.PloS one · 2022Article
- Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.PloS one · 2022Article
- Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition.Scientific reports · 2021Article
- Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.BMC infectious diseases · 2021Article
- Epidemiology of hemorrhagic fever with renal syndrome in Tai'an area.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
- Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China.BMJ open · 2021Article
- Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence.Environmental science and pollution research international · 2021Article
- Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.PloS one · 2021Article
- Article
- Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.Infection and drug resistance · 2021Article
- Seasonal variation in notified tuberculosis cases from 2014 to 2018 in eastern China.The Journal of international medical research · 2020Article
- Analysis of Hemorrhagic Fever With Renal Syndrome Using Wavelet Tools in Mainland China, 2004-2019.Frontiers in public health · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesHaemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.
designTime-series study.
settingThe People's Republic of China.
methodsAutoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.
resultsThe monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)
conclusionsThe hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.
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