Qiu Juan, Li Xiaodong, Zhu Huaiping, Xiao Fei
Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China.
Laboratory of Mathematical Parallel Systems (LAMPS), Department of Mathematics and Statistics, Centre for Diseases Modeling (CDM), York University, Toronto, ON M3J1P3, Canada.
Animals (Basel). 2024 Sep 29;14(19):2814. doi: 10.3390/ani14192814.
Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.
空间流行病学提供了一个用于分析疾病空间分布和传播的综合框架,它利用先进的技术工具和软件,包括地理信息系统(GIS)、遥感技术、统计和数学软件以及空间分析工具。尽管其在猪病毒性疾病(SVD)中的应用日益增多,但其跨学科性质带来了一些挑战。为了帮助新手、一线兽医和公共卫生政策制定者应对其复杂性,我们全面概述了空间流行病学在SVD中的常见应用。这些应用根据其目标分为四类:可视化和阐明时空分布模式、识别风险因素、风险绘图以及追踪病原体的时空演变。我们进一步阐述了实现这些目标所需的技术方法、软件和注意事项。此外,我们还讨论了地理相关分析中的生态谬误和假设生成等关键问题。最后,我们探讨了空间流行病学在“同一个健康”框架下SVD中的未来前景,为从事SVD和其他流行病空间分析的研究人员提供了有价值的参考。