title: Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China.
abstract: 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.
authors.affiliation: 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.|College of Resources and Environment, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing, 100049, China.
authors.forename: Shujuan
authors.initials: S
authors.lastname: Li
authors.affiliation: 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.
authors.forename: Wei
authors.initials: W
authors.lastname: Cao
authors.affiliation: 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.
authors.forename: Hongyan
authors.initials: H
authors.lastname: Ren
authors.affiliation: State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing, 102206, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Lu
authors.affiliation: 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.
authors.forename: Dafang
authors.initials: D
authors.lastname: Zhuang
authors.affiliation: State Key Laboratory for Infectious Diseases Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, 5 Changbai Road, Changping, Beijing, 102206, China.
authors.forename: Qiyong
authors.initials: Q
authors.lastname: Liu
mesh_terms: D002681:China / Q000453:epidemiology; D006480:Hemorrhagic Fever with Renal Syndrome / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D008685:Meteorological Concepts; D015233:Models, Statistical; D012621:Seasons