Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach
Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach
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DOI：
10.1177/0300060519850734
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发表时间：
2019-05
期刊：
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通讯作者：
Youlin Zhao;L. Ge;Junwei Liu;Honghui Liu;Lei Yu;Ning Wang;Yijun Zhou;Xu Ding
中科院分区：
文献类型：
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作者：
Youlin Zhao;L. Ge;Junwei Liu;Honghui Liu;Lei Yu;Ning Wang;Yijun Zhou;Xu Ding
目的肾综合征出血热是由汉坦病毒引起的一种自然疫源地传染病，2011年至2015年湖北省共造成37人死亡，中国。肾综合征出血热疫情呈季节性分布，在空间和时间上表现出异质性。本研究旨在明确肾综合征出血热疫情的时空特征及其可能的影响因素。方法采用时空立方体(STC)方法对不同时空位置的肾综合征出血热疫情进行调查。STC可以用来在三维空间和时间上可视化移动对象(或变化趋势)的轨迹。我们应用时空统计方法，包括时空热点和时空局部离群点分析，基于计算的肾综合征出血热病例STC模型，识别时空热点和离群点分布。我们使用时空重心方法来揭示可能的因素与肾综合征出血热流行之间的关联。结果用STC模型定义了各时空位置的病例数，反映了HFRS流行的动态特征。STC模型提供了关于肾综合征出血热疫情时空模式的准确和详细的结果。结论该方法有可能应用于具有相似时空模式的传染病。
Objective 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. Methods 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. Results 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. Conclusion The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.