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DOI:	10.1371/journal.pone.0163771
论文题名:	Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China
作者:	Shujuan Li; Wei Cao; Hongyan Ren; Liang Lu; Dafang Zhuang; Qiyong Liu
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-10-5
卷:	11, 期:10
英文关键词:	Hemorrhagic fever with renal syndrome ; Seasons ; China ; Humidity ; Rain ; Seasonal variations ; Meteorology ; Forecasting
英文摘要:	Exact prediction of Hemorrhagic fever with renal syndrome (HFRS) epidemics must improve to establish effective preventive measures in China. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish a highly predictive model of HFRS. Meteorological factors were considered external variables through a cross correlation analysis. Then, these factors were included in the SARIMA model to determine if they could improve the predictive ability of HFRS epidemics in the region. The optimal univariate SARIMA model was identified as (0,0,2)(1,1,1)12. The R2 of the prediction of HFRS cases from January 2014 to December 2014 was 0.857, and the Root mean square error (RMSE) was 2.708. However, the inclusion of meteorological variables as external regressors did not significantly improve the SARIMA model. This result is likely because seasonal variations in meteorological variables were included in the seasonal characteristics of the HFRS itself.
URL:	http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163771
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/14618
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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journal.pone.0163771.PDF（1498KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing, 100101, China

Recommended Citation:

Shujuan Li,Wei Cao,Hongyan Ren,et al. Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China[J]. PLOS ONE,2016-01-01,11(10)

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