Lin Chia-Hsien, Wen Tzai-Hung
Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei City 10610, Taiwan.
Department of Geography, National Taiwan University, Taipei City 10617, Taiwan.
Trop Med Infect Dis. 2022 Aug 2;7(8):164. doi: 10.3390/tropicalmed7080164.
Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.
人类的直接和间接传播的传染病都与空间相关。空间维度包括：易感人群与人类共享的环境、受污染材料以及感染性动物物种之间的距离。因此，在管理和理解新发传染病方面的空间概念至关重要。最近，由于计算性能和统计方法的改进，疾病空间数据的可视化和分析有了新的可能性。本综述提供了在管理传染病中常用的空间或时空方法。它涵盖四个部分，即：可视化、总体聚类、热点检测和风险因素识别。前三部分提供了针对点数据（即个体数据）和汇总数据（即个体点的汇总）的方法和流行病学应用。最后一部分重点介绍针对邻域效应或空间异质性进行调整的空间回归方法及其实施。了解传染病传播中的时空变化对疾病管理有三个积极影响。这些是：改进监测系统、生成假设和批准假设以及制定预防和控制策略。值得注意的是，在应用时空方法之前必须考虑伦理和数据质量。开发差分全球定位系统方法和优化贝叶斯估计是未来的方向。
