Development of temporal modeling for prediction of dengue infection in Northeastern Thailand

Wongkoon, Siriwan; Jaroensutasinee, Mullica; Jaroensutasinee, Krisanadej

doi:10.1016/s1995-7645(12)60034-0

Cited by 56 publications

(46 citation statements)

References 18 publications

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“…Decomposition methods [4] and multilevel time series models [5] were used to forecast respiratory syncytial virus. Autoregressive integrated moving average (ARIMA) models have been widely used for epidemic time series forecasting including the hemorrhagic fever with renal syndrome [6], [7], dengue fever [8], [9], and tuberculosis [10]. Models based on artificial neural networks were also used to predict the incidence of hepatitis A [11], [12] and typhoid fever [13].…”

Section: Introductionmentioning

confidence: 99%

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

et al. 2014

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Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases.

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Section: Introductionmentioning

confidence: 99%

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

et al. 2014

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“…In epidemiology, ARIMA models have been successfully applied to predict the morbidity of infectious disease [13,14,15,16,17,18,19,36,37,38,39]. In this study, the monthly morbidity data of TB from January 2004 to June 2014 was collected in Xinjiang, China.…”

Section: Discussionmentioning

confidence: 99%

“…Some popular methods currently used in prediction for infectious disease morbidity, such as linear regression method [4,5,6], gray model method [7,8,9], artificial neural network method [10,11,12], specifically the autoregressive integrated moving average (ARIMA) method [13,14,15,16,17,18,19], etc. ARIMA method is a reflection of the time dynamic dependency; it can reveal the quantitative relationship between the research object and other objects with the development and change of time.…”

Section: Introductionmentioning

confidence: 99%

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Zheng

Zhang

et al. 2015

PLoS ONE

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Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.

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“…The evidence for the use of marine flora to be precise in treatment of human aliments is extensive. In Asian maritime areas, seagrass are used as curative agents for various maladies such as anti malarial [11,12,20] , antibacterial [16,17] , antihelmintic, cough, antipyretic, wound healing, treatment of gallstone and goiter [24][25][26][27][28][29][30][31][32] . The studies on seaweed extracts with larvicidal activities are too restricted.…”

Section: Discussionmentioning

confidence: 99%

Bioactivity of seagrass against the dengue fever mosquito Aedes aegypti larvae

Ali

Ravikumar

Beula³

2012

Asian Pacific Journal of Tropical Biomedicine

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Development of temporal modeling for prediction of dengue infection in Northeastern Thailand

Abstract: Early warning based on the data in the previous months could assist in improving vector control, community intervention, and personal protection.

Cited by 56 publications

References 18 publications

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

Bioactivity of seagrass against the dengue fever mosquito Aedes aegypti larvae

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