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

Abstract

Fuyan Shi,1 Changlan Yu,2 Liping Yang,3 Fangyou Li,2 Jiangtao Lun,4 Wenfeng Gao,5 Yongyong Xu,6 Yufei Xiao,1 Sravya B Shankara,7 Qingfeng Zheng,8 Bo Zhang,9 Suzhen Wang1 1Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People’s Republic of China; 2Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People’s Republic of China; 3Health and Medical Center, Xijing Hospital, Air Force Military Medical University, Xi’an, Shannxi, People’s Republic of China; 4Anqiu Meteorological Bureau, Anqiu, Shandong, People’s Republic of China; 5Department of Immunology and Rheumatology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, People’s Republic of China; 6Department of Health Statistics, School of Military Preventive Medicine, Air Force Military Medical University, Xi’an, Shannxi, People’s Republic of China; 7Program in Health: Science, Society, and Policy, Brandeis University, Waltham, MA, USA; 8Institute for Hospital Management of Tsinghua University, Tsinghua Campus, Shenzhen, People’s Republic of China; 9Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USACorrespondence: Bo Zhang; Suzhen Wang Email [email protected]; [email protected]: The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS.Methods: Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period.Results: Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)12 model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS.Conclusion: This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.Keywords: hemorrhagic fever with renal syndrome, meteorological factors, autoregressive integrated moving average model, generalized additive model, prediction

Keywords

• hemorrhagic fever with renal syndrome
• meteorological factors
• autoregressive integrated moving average model
• generalized additive model
• prediction.