ArticlePloS one2014
A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, 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 18 papers.
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Who cites it
18 citing papers in PubMed, 26 citations in OpenAlex.
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- Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition.Scientific reports · 2021Article
- Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.PloS one · 2021Article
- Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis.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
- Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.BMJ open · 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
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- Time series model for forecasting the number of new admission inpatients.BMC medical informatics and decision making · 2018Article
- Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model.PloS one · 2018Article
- A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.The American journal of tropical medicine and hygiene · 2017Article
- Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006-2014.International journal of environmental research and public health · 2017Article
- Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach.Scientific reports · 2016Article
- Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.International journal of environmental research and public health · 2016Article
- An Approach to Improve the Performance of PM Forecasters.PloS one · 2015Article
- Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.PloS one · 2015Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUNDS/
objectiveSchistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.
methodsA hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.
resultsThe modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869 × 10(-4), 0.0029, 0.0419 with a corresponding testing error of 0.9375 × 10(-4), 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.
conclusionThe hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.
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