Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004&amp;ndash;2014

Wang, Tao; Zhou, Yunping; Huang, Zhenshui; Cui, Feng; Zhai, Shenyong

doi:10.7883/yoken.jjid.2014.567

Cited by 14 publications

(17 citation statements)

References 25 publications

(38 reference statements)

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“…It is very useful in modeling temporal dependence structure of a time series. At present, this method has been widely used in the prediction of infectious diseases, and has achieved successful prediction results, for instance, Tian C W et al [4] forecasted monthly cases of hand-foot-mouth disease successfully in China; Wang T et al [5] suggested that ARIMA(3,1,1)(2,1,1) 12 model was reliable with a high validity, which could be used to predict hemorrhagic fever with renal syndrome incidence in Zibo; Myriam Gharbil et al [6] predicted the dengue incidence in Guadeloupe based on time series analysis; López-Montenegro LE [7] predicted dengue cases in Colombia from 2018 to 2022 based on Auto-Regressive Integrated Moving Average (ARIMA) model; Zheng Y-L et al [8] and Liao Z [9] forcasted TB incidence successfully using SARIMA model, etc. [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

et al. 2020

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Background: Tuberculosis (TB) remains a serious public health problem with substantial financial burden in China. The incidence of TB in Guangxi province is much higher than that in the national level, however, there is no predictive study of TB in recent years in Guangxi, therefore, it is urgent to construct a model to predict the incidence of TB, which could provide help for the prevention and control of TB. Methods: Box-Jenkins model methods have been successfully applied to predict the incidence of infectious disease. In this study, based on the analysis of TB incidence in Guangxi from January 2012 to June 2019, we constructed TB prediction model by Box-Jenkins methods, and used root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to test the performance and prediction accuracy of model. Results: From January 2012 to June 2019, a total of 587,344 cases of TB were reported and 879 cases died in Guangxi. Based on TB incidence from January 2012 to December 2018, the SARIMA((2),0,(2))(0,1,0) 12 model was established, the AIC and SC of this model were 2.87 and 2.98, the fitting accuracy indexes, such as RMSE, MAE and MAPE were 0.98, 0.77 and 5.8 respectively; the prediction accuracy indexes, such as RMSE, MAE and MAPE were 0.62, 0.45 and 3.77, respectively. Based on the SARIMA((2),0,(2))(0,1,0) 12 model, we predicted the TB incidence in Guangxi from July 2019 to December 2020. Conclusions: This study filled the gap in the prediction of TB incidence in Guangxi in recent years. The established SARIMA((2),0,(2))(0,1,0) 12 model has high prediction accuracy and good prediction performance. The results suggested the change trend of TB incidence predicted by SARIMA((2),0,(2))(0,1,0) 12 model from July 2019 to December 2020 was similar to that in the previous two years, and TB incidence will experience slight decrease, the predicted results can provide scientific reference for the prevention and control of TB in Guangxi, China.

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Section: Introductionmentioning

confidence: 99%

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

et al. 2020

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“…In China, TB is categorised to class B notifiable diseases, and the incidence ranks second among all of the class B notifiable diseases [3]. The seasonal autoregressive integrated moving average (SARIMA) model is widely used to predict the incidence of infectious diseases [4][5][6][7], and also adopted as the main method in TB prediction around the world [8]. However, TB seasonality in China was mainly reported in local areas and the recent nationwide trend has not been reported [8,9].…”

Section: Introductionmentioning

confidence: 99%

Time-series analysis of tuberculosis from 2005 to 2017 in China

et al. 2018

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Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.

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“…At this time, the existing mathematical models, specifically the autoregressive integrated moving average (ARIMA) model that is widely used to predict incidence of infectious diseases, [13][14][15] are based on linear assumptions. Moreover, the generalized regression neural network (GRNN) model, which is generally used to analyze the nonlinear data sets, could also be used.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Jiang

Gao

et al. 2017

The American Journal of Tropical Medicine and Hygiene

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It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.

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Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014

Cited by 14 publications

References 25 publications

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China

Time-series analysis of tuberculosis from 2005 to 2017 in China

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

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