A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

Zheng, Yanling; Zhang, Liping; Zhu, XiXun; Guo, Gang

doi:10.1371/journal.pone.0234660

Cited by 13 publications

(14 citation statements)

References 34 publications

(26 reference statements)

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“…In other fields, such as economics and transportation, the ARIMA-ERNN model has been found to provide better predictive accuracy than other models [ 30 , 31 ]. However, epidemiologists have only rarely used the ARIMA-ERNN model for the prediction of infectious diseases [ 32 ]. To the best of our knowledge, the present study constitutes the first use of a combined ARIMA-ERNN model to predict the incidence of pertussis.…”

Section: Discussionmentioning

confidence: 99%

ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

Wang

Pan²,

Li³

et al. 2022

BMC Public Health

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Objective To compare an autoregressive integrated moving average (ARIMA) model with a model that combines ARIMA with the Elman recurrent neural network (ARIMA-ERNN) in predicting the incidence of pertussis in mainland China. Background The incidence of pertussis has increased rapidly in mainland China since 2016, making the disease an increasing public health threat. There is a pressing need for models capable of accurately predicting the incidence of pertussis in order to guide prevention and control measures. We developed and compared two models for predicting pertussis incidence in mainland China. Methods Data on the incidence of pertussis in mainland China from 2004 to 2019 were obtained from the official website of the Chinese Center for Disease Control and Prevention. An ARIMA model was established using SAS (ver. 9.4) software and an ARIMA-ERNN model was established using MATLAB (ver. R2019a) software. The performances of these models were compared. Results From 2004 to 2019, there were 104,837 reported cases of pertussis in mainland China, with an increasing incidence over time. The incidence of pertussis showed obvious seasonal characteristics, with the peak lasting from March to September every year. Compared with the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ARIMA model, those of the ARIMA-ERNN model were 81.43%, 95.97% and 80.86% lower, respectively, in fitting performance. In terms of prediction performance, the MAE, MSE and MAPE were 37.75%, 56.88% and 43.75% lower, respectively. Conclusion The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA model. This provides theoretical support for the prediction of infectious diseases and should be beneficial to public health decision making.

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Section: Discussionmentioning

confidence: 99%

ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

Wang

Pan²,

Li³

et al. 2022

BMC Public Health

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“…It should be noted that according to the data features of the study, choosing the right model is a prerequisite for exploring credible results. Based on a literature review and our previous research, we found that the ARIMA, [3] GM (1, 1) model, [3] Decision tree, [32] Random forest, [32] AdaBoost with decision tree (AdaBoost), [32] extreme gradient boosting decision tree (XGBoost), [32] Elman network, [33] and generalized regression neural network (GRNN) [34] can be used to predict hepatitis B. Among these, the GM (1,1) model is suitable for short-term prediction with small sample sizes [17] .…”

Section: Discussionmentioning

confidence: 99%

“…We then apply the combined forecasting model for prediction, such as the SARIMA-NARNNX hybrid model, [45] SARIMA-NAR hybrid model, [46] and SARIMA-SDGM hybrid prediction model [47] . Third, almost all prediction techniques have defects; therefore, the prediction results only take references [33] . We should adopt a correct attitude towards the prediction results and use them to guide practical work, rather than regarding the prediction results as an exclusive policy-making reference frame.…”

Section: Discussionmentioning

confidence: 99%

“…Scientific prediction and analysis play a crucial role in the prevention and control of hepatitis B, allocation of health resources properly or methodically, provision of effective measures, and the promotion of public health [3] . Meanwhile, a reasonable prediction of hepatitis B can give full play to early warning links in advance to provide corresponding preventive measures [14] . Therefore, it may remind us the prediction and analysis of hepatitis B are crucial for prevention and control.…”

Section: Introductionmentioning

confidence: 99%

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The research of SARIMA model for prediction of hepatitis B in mainland China

et al. 2022

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Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures. Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model. Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval. This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

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“…In the study of forecasting the incidence of hepatitis B, Wang et al found that ARIMA model showed better forecasting performance than GM(1,1) model [ 16 ]. Zheng et al showed that ELMAN model was superior to ARIMA (0,1,1) model in predicting the incidence of hepatitis B in Guangxi through comparative experiments [ 17 ]. In addition, Wei et al announced that the ARIMA-GRNN mixed model performs better than the ARIMA model and the GRNN model in predicting the incidence of hepatitis [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

Jiang-Ning

Shi

Huang

et al. 2021

Complex Intell. Syst.

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Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.

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A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

Cited by 13 publications

References 34 publications

ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

The research of SARIMA model for prediction of hepatitis B in mainland China

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

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