由汉坦病毒引起的肾综合征出血热(HFRS)在中国是一个严重的公共卫生问题,占全球报告的HFRS病例的90%。在这项研究中,我们应用了地理信息系统(GIS),空间自相关分析和季节自回归综合移动平均(SARIMA)模型来描述和预测HFRS流行,目的是监测和预测中国大陆的HFRS。2004-2016年中国HFRS数据来自国家传染病报告系统(NIDRS)数据库和中国疾病预防控制中心(CDC)。制作了GIS地图以检测HFRS病例的空间分布。在空间全局自相关分析中采用Moran\sI来识别HFRS暴发的整体时空模式,而当地的Moran'sIi是在省一级识别HFRS的“热点”地区。通过Akaike信息准则和Ljung-Box检验,建立了一个合适的SARIMA模型来预测2016年HFRS发病率。在2004-2015年期间,共报告了165710例HFRS病例,省一级的年平均发病率为每10万人0至13.05例。全球MoranI的分析表明,HFRS疫情呈现空间集群分布,从2004年到2009年,集群化程度逐渐降低,然后呈随机分布,2012年达到最低点。当地的Moran'sIi确定,中国东北四个省作为传统的流行病中心,为“高-高”集群做出了贡献,自2011年以来,陕西成为另一个HFRS热点地区。2004年至2009年,中国大陆HFRS的月发病率急剧下降,然后在2010年至2012年明显上升,2013年以来再次下降,季节性波动明显。SARIMA((0,1,3)×(1,0,1)12)模型是中国大陆HFRS数据集最适合的预测模型。近年来,中国大陆HFRS的时空分布变化;结合SARIMA预测模型,本研究为中国HFRS的控制和风险管理计划提供了几种潜在的决策支持工具。
Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in
China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland
China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran\'s I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran\'s Ii was performed to identify \'hotspot\' regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung-Box test. During 2004-2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran\'s I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran\'s Ii identified that four provinces in northeast
China contributed to a \'high-high\' cluster as a traditional epidemic centre, and Shaanxi became another HFRS \'hotspot\' region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland
China. The spatiotemporal distribution of HFRS in mainland
China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.