Application of the exponential smoothing model and ARIMA model in prediction of the endemic situation of schistosomiasis in Hunan Province

To predict the changes in the prevalence of infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. The data pertaining to infections in humans and livestock in Hunan Province from 1957 to 2015 were collected, and the exponential smoothing mode...

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Published inZhongguo xue xi chong bing fang zhi za zhi Vol. 32; no. 3; p. 236
Main Authors Zhou, J, Ren, G H, He, H B, Hou, X Y, Deng, W C
Format Journal Article
LanguageChinese
Published China 27.04.2020
Subjects
Hunan Province
Schistosomiasis
ARIMA model
Exponential smoothing model
Prediction
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Summary:To predict the changes in the prevalence of infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. The data pertaining to infections in humans and livestock in Hunan Province from 1957 to 2015 were collected, and the exponential smoothing model and the ARIMA model were created using the software Eviews and PASW Statistics 18.0. In addition, the effectiveness of these two models for the prediction of infections in humans and livestock in Hunan Province from 2016 to 2018 was evaluated. The exponential smoothing model and the ARIMA model had a high goodness of fit for prediction of infections in humans and livestock in Hunan Province from 1957 to 2015. There was a linear trend in the prevalence of infections in humans and livestock in Hunan Province from 1957 to 2015. The prevalence of infections in humans predicted with the Brown's linear trend and the prevalence of infections in livestock predicted with the Holt's linear trend in Hunan Province from 2016
ISSN:1005-6661
DOI:10.16250/j.32.1374.2020021