PDF(Pubmed)
Abstract：
Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals\' movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.
摘要：
及时准确地发现新出现的感染对于有效的暴发管理和疾病控制至关重要。人类流动性显著影响传染病的空间传播动态。空间采样，整合目标的空间结构，作为检测感染的一种测试分配的方法，利用有关个人运动和接触行为的信息可以提高瞄准精度。本研究引入了一个由人类流动数据的时空分析提供信息的空间抽样框架，旨在优化检测资源的分配，以检测新出现的感染。流动性模式，从对兴趣点和旅行数据进行聚类得出，在社区一级被整合到四种空间抽样方法中。我们通过分析实际和模拟的爆发来评估所提出的基于移动性的空间采样，考虑到可传播性的情况，干预时机，和城市人口密度。结果表明，利用社区间流动数据和初始病例位置，建议的病例流强度（CFI）和病例透射强度（CTI）的空间采样通过减少筛选的个体数量，同时保持感染识别的高准确率，从而提高了社区水平的测试效率。此外，CFI和CTI在城市中的迅速应用对于有效检测至关重要，特别是在人口稠密地区的高度传染性感染中。随着人类流动数据广泛用于传染病反应，提出的理论框架将流动模式的时空数据分析扩展到空间采样，提供具有成本效益的解决方案，以优化测试资源部署，以遏制新出现的传染病。
