2012
DOI: 10.1016/s1995-7645(12)60034-0
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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.

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Cited by 54 publications
(43 citation statements)
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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%
“…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%
“…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%
“…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%