Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China

PLOS ONE, Dec 2019

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.

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Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China

October Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China Shujuan Li 0 1 Wei Cao 0 1 Hongyan Ren 0 1 Liang Lu 1 Dafang Zhuang 0 1 Qiyong Liu 1 0 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 , 2 College of Resources and Environment, University of Chinese Academy of Sciences , No. 19 Yuquan Road, Beijing, 100049, China , 3 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 1 Editor: Ulrike Gertrud Munderloh, University of Minnesota , UNITED STATES HFRS itself. Introduction 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 Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by different species of Hantavirus, Hantaan virus (HNTV) transmitted by the striped field mouse (Apodemus agrarius), Seoul virus (SEOV) transmitted by the Norway rat (Rattus norvegicus), resulting in high fever and varying degrees of renal damage and hemorrhaging[ 1 ]. Approximately 90% of the world’s cases have been reported in China [ 2 ], with 10,000 cases annually in mainland China [ 3 ]. In Shandong, the HFRS epidemic exhibited a rebound trend, potentially due to changes associated with climate change and variations in rodent populations [ 4 ]. HFRS epidemics can be affected by environmental, population and reservoir factors, among which meteorological factors play an important role in the transmission of HFRS [ 5–10 ]. These meteorological factors, including temperature, precipitation and relative humidity, not experiments, and also contributed to the writing of the manuscript; and by Key Laboratory of Public Health Safety (Fudan Univeristy), Ministry of Education, China (Grant No. GW2014-4), http:// sph.fudan.edu.cn/, H.R.; the funder conceived and designed the experiments, and also contributed to the writing of the manuscript. Competing Interests: The authors have declared that no competing interests exist. only affect the transmission of Hantavirus but also impact the reservoir, rodents and contact chances between humans and rodents[ 11–13 ]. The infectivity and survival time of the Hantavirus after it leaves the host is largely dependent on the environmental temperature and humidity, and the chance of contact between humans and Hantavirus is influenced by the rainfall, temperature and humidity[ 11–13 ]. Few studies have investigated the impact of meteorological factors on the dynamics of HFRS in the context of the increasing trend in recent years. Modeling and forecasting the HFRS epidemic is essential to controlling and preventing HFRS. Autoregressive Integrated Moving Average (ARIMA) models have been successfully applied to predict the incidence of infectious diseases, including HFRS [ 14,15 ] and other diseases [ 16–20 ]. Since HFRS presents typical seasonal characteristics [ 2,21–23 ], a Seasonal Autoregressive Integrated Moving Average (SARIMA) model can effectively simulate the HFRS epidemic. In this study, we investigated seasonal HFRS variations and developed SARIMA models of the number of HFRS cases using time series analysis. The goal of this study was to characterize whether the inclusion of the affecting factors is useful in predicting epidemics with higher precision. The predictive model would be used to facilitate efficient HFRS control. Materials and Methods Study area Jiaonan County is located in Qingdao, Shandong province (35°350–36°080N and 119°300–120° 110E; Fig 1). The county is characterized by a coastal climate, with an average temperature of 12.1°C, annual precipitation of 750–900 mm and relative humidity of 75%. Data collection Monthly HFRS epidemiologic data from Jiaonan were provided by the Jiaonan Center for Disease Control and Prevention, spanning from January 1992 to December 2 (...truncated)


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Shujuan Li, Wei Cao, Hongyan Ren, Liang Lu, Dafang Zhuang, Qiyong Liu. Time Series Analysis of Hemorrhagic Fever with Renal Syndrome: A Case Study in Jiaonan County, China, PLOS ONE, 2016, Volume 11, Issue 10, DOI: 10.1371/journal.pone.0163771