Zhao Youlin, Ge Liang, Liu Junwei, Liu Honghui, Yu Lei, Wang Ning, Zhou Yijun, Ding Xu
1 Business School of Hohai University, Nanjing city, Jiangsu Province, China.
2 Tianjin Institute of Surveying and Mapping, Liqizhuang, Tianjin, China.
J Int Med Res. 2019 Jul;47(7):3371-3388. doi: 10.1177/0300060519850734. Epub 2019 May 30.
Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors.
We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics.
In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics.
The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.
肾综合征出血热（HFRS）是一种由汉坦病毒引起的自然疫源性传染病，2011年至2015年间在中国湖北省导致37人死亡。HFRS疫情呈季节性分布，在空间和时间上表现出异质性。我们旨在确定HFRS疫情的时空特征及其可能的影响因素。
我们使用时空立方体（STC）方法调查不同时空位置的HFRS疫情。STC可用于在三维空间中可视化移动物体的轨迹（或变化趋势）在空间和时间上的情况。我们基于计算出的HFRS病例STC模型，应用时空统计方法，包括时空热点分析和时空局部离群值分析，以识别时空热点和离群值分布。我们使用时空重心法揭示可能因素与HFRS疫情之间的关联。
在本研究中，每个时空位置的HFRS病例由STC模型定义，该模型可以呈现HFRS疫情的动态特征。STC模型为HFRS疫情的时空模式提供了准确而详细的结果。
本文中的方法可能适用于具有类似时空模式的传染病。
