Residential Mobility: Implications for the Accuracy of Disease Cluster Detection

公共卫生组织越来越依赖时空集群技术来针对预防等以地点为基础的卫生倡议。然而，方法技术得益于改进。在这项提案中，研究人员检查了对时空集群检测有效性的常见担忧的影响，同时开发了解决或减少其对集群分析影响的方法和实践。具体地说，研究人员首先研究了居住流动性对时空聚类有效性的影响。其次，研究人员估计了在集群变得不被注意之前，研究居住历史应该达到的粒度水平。该项目的第三个目标是评估由患者流动性构建的时空关系如何影响现有时空集群的相对风险。该跨学科项目有助于健康地理学和时空分析方面的研究。这项研究整合了个人流动数据的新来源，同时创建了将这些数据纳入现有工作流程的方法工具。这些方法和工具有助于公共卫生努力应对未来的疾病暴发。关键结论和建议与地方、地区和联邦利益攸关方共享。多名学生参与了该项目，为早期职业社会科学家的教育和培训做出了贡献。人类流动性、疾病动力学和隐私问题的复杂性一直是对集群检测方法有效性的挑战。现在，通过更好地获取电子健康记录、个人居住历史、准确的关联算法和先进的地理空间技术，有可能测试这些假设的后果。在这个项目中，研究人员研究了不同的空间和时间分辨率如何影响时空簇检测的准确性。该项目还审查了住宅流动性的整合如何可以减少时空不确定性，目的是查明最大限度地检测数据中的时空聚集所需的详细程度。在方法上，该项目使用模拟的时空轨迹来测试集群算法，同时评估缩放问题和其他扰动如何影响集群测试的有效性。通过对两个大型经验数据集的分析来检验该方法的普适性，这两个数据集包括关于诊断后患者流动性的详细信息。该项目通过强调应在多大程度上监测住宅流动性以进行准确推断，为研究设计考虑提供信息。除了应用于疾病检测之外，这些方法还扩展到使用聚类方法的其他研究领域，如空间生态学和动物运动、交通和城市动力学以及犯罪学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Public health organizations are increasingly relying on space-time clustering techniques to target place-based health initiatives such as prevention. However, the methodological techniques benefit from refinement. In this proposal, the researchers examine the impact of commonly cited concerns about the validity of space-time cluster detection while developing methods and practices to resolve or reduce their effects on cluster analytics. Specifically, the researchers first study the impact of residential mobility on the validity of space-time clustering. Second, the researchers estimate the level of granularity at which residential histories should be studied before clusters become unnoticed. A third goal of the project is an evaluation of how space-time relationships constructed from patient mobility can affect the relative risk of existing space-time clusters. The interdisciplinary project contributes to research in health geography and space-time analytics. The study integrates new sources of individual movement data while creating methodological tools to incorporate those data into existing workflows. These methods and tools contribute to public health efforts to respond to disease outbreaks in the future. Key findings and recommendations are shared with local, regional, and federal stakeholders. Multiple students are involved in the project, contributing to the education and training of early-career social scientists.The complexity of human mobility, disease dynamics, and privacy concerns are persistent challenges to the validity of cluster detection methods. The opportunity to test the consequences of these assumptions is now possible through improved access to electronic health records, individual residential histories, accurate linkage algorithms, and advanced geospatial technologies. In this project, the researchers examine how heterogeneous spatial and temporal resolution affects space-time cluster detection accuracy. The project also examines how the integration of residential mobility may mitigate space-time uncertainty, with the aim of discerning the level of detail needed to maximize the detection of spatiotemporal clustering in the data. Methodologically, the project uses simulated space-time trajectories to test clustering algorithms, simultaneously evaluating how scaling issues and other perturbations affect the validity of clustering tests. The generalizability of the approach is examined via an analysis of two large empirical datasets that include detailed detail on the mobility of patients after diagnosis. The project informs research design considerations by highlighting the extent to which residential mobility should be monitored for accurate inferences. In addition to applications to disease detection, the methods extend to other areas of research that use clustering methods, such as spatial ecology and animal movement, transportation and urban dynamics, and criminology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.