A hybrid seasonal prediction model for tuberculosis incidence in China

Cao, Shiyi; Wang, Feng; Tam, Wilson W.S.; Tse, Lap Ah; Kim, Jean Hee; Liu, Junan; Lu, Zuxun

doi:10.1186/1472-6947-13-56

Cited by 53 publications

(64 citation statements)

References 13 publications

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“…However, few hybrid prediction models have been used to predict TB incidence in China. Shiyi Cao, et al [31] constructed a SARIMA-GRNN model with the data of national TB reported cases from Jan 2005 to Dec 2011. Because the ability of reporting TB cases is different in various places and the magnitude and pattern of TB vary with regions in China.…”

Section: Discussionmentioning

confidence: 99%

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

et al. 2013

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BackgroundA prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources.MethodsThe autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated.ResultsA total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)12 model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model.Discussion and ConclusionsThe gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China.

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Section: Discussionmentioning

confidence: 99%

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

et al. 2013

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“…The ARIMA model includes autoregressive (AR) model, moving average (MA) model, and seasonal autoregressive integrated moving average (SARIMA) model [2]. The Augmented Dickey-Fuller (ADF) [3] unit-root test helps in estimating whether the time series is stationary. Log transformation and differences are the preferred approaches to stabilize the time series [4].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Application of the ARIMA model on the COVID-2019 epidemic dataset

et al. 2020

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a b s t r a c tCoronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.

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“…One of these methods is the Increased Dickey-Fuller (ADF) unit root test. 8 Log transformation and differences are the preferred approaches to stationary the time series. 9 Seasonal and nonseasonal differences were used to stationary the term trend and periodicity.…”

Section: Methodsmentioning

confidence: 99%

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Bayyurt

2020

Preprint

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After the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field. KEYWORDS: COVID-19, pandemi, ARIMA, time series analysis

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A hybrid seasonal prediction model for tuberculosis incidence in China

Cited by 53 publications

References 13 publications

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

Application of the ARIMA model on the COVID-2019 epidemic dataset

Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

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