Application of the exponential smoothing model and ARIMA model in prediction of the endemic situation of schistosomiasis in Hunan Province
10.16250/j.32.1374.2020021
- VernacularTitle:指数平滑模型与 ARIMA 模型在湖南省血吸虫病流行趋势预测中的应用
- Author:
Jie ZHOU
1
;
Guang-Hui REN
1
;
Hong-Bin HE
1
;
Xun-Ya HOU
1
;
Wei-Cheng DENG
1
Author Information
1. Hunan Institute of Parasitic Diseases, WHO Collaborating Center on Schistosomiasis Control in Lake Regions, Hunan Key Laboratory of Immunology and Transmission Control of Schistosomiasis, National Key Clinical Specialty, Yueyang 414000, China
- Publication Type:Journal Article
- Keywords:
Schistosomiasis;
Exponential smoothing model;
ARIMA model;
Prediction;
Hunan Province
- From:
Chinese Journal of Schistosomiasis Control
2020;32(3):236-241
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict the changes in the prevalence of Schistosoma japonicum infections in humans and livestock in Hunan Province using the exponential smoothing model and the ARIMA model. Methods The data pertaining to S. japonicum 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 S. japonicum infections in humans and livestock in Hunan Province from 2016 to 2018 was evaluated. Results The exponential smoothing model and the ARIMA model had a high goodness of fit for prediction of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. There was a linear trend in the prevalence of S. japonicum infections in humans and livestock in Hunan Province from 1957 to 2015. The prevalence of S. japonicum infections in humans predicted with the Brown’s linear trend and the prevalence of S. japonicum infections in livestock predicted with the Holt’s linear trend in Hunan Province from 2016 to 2018 fitted better the actual data than the ARIMA model; however, prediction of the ARIMA model indicated that the endemic situation of schistosomiasis remained at a low level in Hunan Province. Conclusion At a low epidemic level, development of highly sensitive tools for monitoring schistosomiasis is urgently needed in Hunan Province to fit the current endemic situation, and the schistosomiasis control measures should be intensified to consolidate the control achievements.
