ArticlePloS one2014
Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed, 78 citations in OpenAlex.
- Article
- Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya.PLOS digital health · 2023Article
- Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam.Expert systems with applications · 2022Article
- Impact of the coronavirus disease 2019 interventions on the incidence of hand, foot, and mouth disease in mainland China.The Lancet regional health. Western Pacific · 2022Article
- Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.Journal of biomedical informatics · 2021Article
- 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
- Early warning of hand, foot, and mouth disease transmission: A modeling study in mainland, China.PLoS neglected tropical diseases · 2021Article
- Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China.Environmental science and pollution research international · 2021Article
- Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.PloS one · 2021Article
- Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.Infection and drug resistance · 2021Article
- Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches.Chaos, solitons, and fractals · 2020Article
- Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.BMC infectious diseases · 2019Article
- Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.Scientific reports · 2019Article
- Observational
- Article
- Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population.Infection and drug resistance · 2019Article
- Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach.BMC infectious diseases · 2018Article
- Time series model for forecasting the number of new admission inpatients.BMC medical informatics and decision making · 2018Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
backgroundOutbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.
methodIn this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.
resultsThe best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.
conclusionThe model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.