Fuyan Shi1, Changlan Yu2, Liping Yang3, Fangyou Li2, Jiangtao Lun4, Wenfeng Gao5, Yongyong Xu6, Yufei Xiao1, Sravya B Shankara7, Qingfeng Zheng8, Bo Zhang9, Suzhen Wang1. 1. Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China. 2. Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People's Republic of China. 3. Health and Medical Center, Xijing Hospital, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China. 4. Anqiu Meteorological Bureau, Anqiu, Shandong, People's Republic of China. 5. Department of Immunology and Rheumatology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, People's Republic of China. 6. Department of Health Statistics, School of Military Preventive Medicine, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China. 7. Program in Health: Science, Society, and Policy, Brandeis University, Waltham, MA, USA. 8. Institute for Hospital Management of Tsinghua University, Tsinghua Campus, Shenzhen, People's Republic of China. 9. Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
OBJECTIVE: 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.
OBJECTIVE: 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.
Authors: Jianjun Xiang; Alana Hansen; Qiyong Liu; Michael Xiaoliang Tong; Xiaobo Liu; Yehuan Sun; Scott Cameron; Scott Hanson-Easey; Gil-Soo Han; Craig Williams; Philip Weinstein; Peng Bi Journal: Sci Total Environ Date: 2018-05-09 Impact factor: 7.963