PDF(Pubmed)
Abstract：
UNASSIGNED: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections.
UNASSIGNED: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities.
UNASSIGNED: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas.
UNASSIGNED: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.
UNASSIGNED: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities.
UNASSIGNED: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas.
UNASSIGNED: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.
摘要：
背景：及时和精确地检测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感染风险和传播动态，空间采样是确定特定区域潜在感染的有价值的工具。本研究探索了空间抽样方法，以各种流动模式为依据，优化检测资源的分配，以检测新出现的感染。方法移动性模式，从对兴趣点数据和旅行数据进行聚类得出，被整合到四种空间采样方法中，以检测社区一级的新出现的感染。为了评估拟议的基于移动性的空间采样的有效性，我们在不同的传播场景下使用实际和模拟的爆发进行了分析，干预时机，和城市人口密度。结果通过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)和病例传播强度(CTI)的采样方法可以大大减少实际和模拟暴发所需的测试数量.尽管如此，在社区内迅速使用CFI和CTI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。结论基于移动性的空间抽样方法可以大大提高社区水平检测的效率，以检测新出现的感染。它通过减少筛选的个体数量来实现这一点，同时保持感染识别的高准确率。它代表了一种经济高效的解决方案，可优化测试资源的部署，必要时,在不同的环境中控制新出现的传染病。
