Literature DB >> 26370428

Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004-2014.

Tao Wang1, Yunping Zhou, Ling Wang, Zhenshui Huang, Feng Cui, Shenyong Zhai.   

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China, where human cases account for 90% of the total global cases. Zibo City is one of the most seriously affected areas in Shandong Province, China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence in Zibo to make the control of HFRS more effective. In this study, we constructed an autoregressive integrated moving average (ARIMA) model for monthly HFRS incidence in Zibo from 2004 to 2013. The ARIMA (3,1,1) × (2,1,1)12 model is reliable with a high validity, which can be used to predict the next year's HFRS incidence in Zibo. The forecast results suggest that the HFRS incidence in Zibo will experience a slight growth in the next year.

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Year:  2015        PMID: 26370428     DOI: 10.7883/yoken.JJID.2014.567

Source DB:  PubMed          Journal:  Jpn J Infect Dis        ISSN: 1344-6304            Impact factor:   1.362


  7 in total

1.  A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.

Authors:  Wudi Wei; Junjun Jiang; Lian Gao; Bingyu Liang; Jiegang Huang; Ning Zang; Chuanyi Ning; Yanyan Liao; Jingzhen Lai; Jun Yu; Fengxiang Qin; Hui Chen; Jinming Su; Li Ye; Hao Liang
Journal:  Am J Trop Med Hyg       Date:  2017-08-18       Impact factor: 2.345

2.  Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models.

Authors:  Fuyan Shi; Changlan Yu; Liping Yang; Fangyou Li; Jiangtao Lun; Wenfeng Gao; Yongyong Xu; Yufei Xiao; Sravya B Shankara; Qingfeng Zheng; Bo Zhang; Suzhen Wang
Journal:  Infect Drug Resist       Date:  2020-07-21       Impact factor: 4.003

3.  Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China.

Authors:  Yuhan Xiao; Yanyan Li; Yuhong Li; Chongchong Yu; Yichun Bai; Lei Wang; Yongbin Wang
Journal:  Infect Drug Resist       Date:  2021-09-21       Impact factor: 4.003

4.  Time-series analysis of tuberculosis from 2005 to 2017 in China.

Authors:  H Wang; C W Tian; W M Wang; X M Luo
Journal:  Epidemiol Infect       Date:  2018-04-30       Impact factor: 4.434

5.  A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS.

Authors:  Zeming Li; Yanning Li
Journal:  BMC Med Inform Decis Mak       Date:  2020-07-02       Impact factor: 2.796

6.  A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS).

Authors:  Youlin Zhao; Liang Ge; Yijun Zhou; Zhongfang Sun; Erlong Zheng; Xingmeng Wang; Yongchun Huang; Huiping Cheng
Journal:  PLoS One       Date:  2018-11-26       Impact factor: 3.240

7.  Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.

Authors:  Rui Zhang; Hejia Song; Qiulan Chen; Yu Wang; Songwang Wang; Yonghong Li
Journal:  PLoS One       Date:  2022-01-14       Impact factor: 3.240

  7 in total

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