AI 中文总结
该研究开发了整合人类日间移动的网络建模框架，推导多尺度瞬时再生数等指标，结合手机数据可设计更精准的传染病时空干预措施。
AI 中文摘要
人类流动驱动着众多传染病的空间传播与持续存在，然而现有用于推断瞬时再生数R(t)的理论和实时操作工具，往往假设人群是静态和/或均匀混合的，无法描述个体如何根据一天内的移动模式异质地产生和获得感染。更新方程是许多这类流行的R(t)估计器的基础，在此我们开发了一个基于网络的建模框架，从中推导得出机制驱动的新型更新方程和传染病暴发的控制指标。这些方程直接整合了一天内的人类移动，以严格定义一系列瞬时再生数：针对单个地点的内向、外向和类型R(t)，地点间的R(t)、聚集地点的R(t)，以及整个流动网络的R(t)。这些量纠正了现有特定地点R(t)估计器的不适性，这类估计器适用于封闭、静态的人群。将我们的框架应用于不同类型网络上的传染病暴发，并结合手机数据，我们展示了新框架的输出如何在网络、地点和传播廊道尺度提供新的多尺度控制指标，以及如何用于设计有针对性的疾病控制干预措施，包括跨空间和时间所需干预的强度、类型和时长。我们捕捉了现有多种测量特定地点和网络层面传播潜力的方式所产生的偏差，这些方式未考虑一天内的人类移动。这个可推广的框架重新定义了由个体在相连地点间移动所塑造的现实世界暴发中的再生数，支持制定更具空间和时间精度的干预措施。
英文摘要
Human movement drives the spatial spread and persistence of many infectious diseases, yet existing theory and real-time operational tools for inferring the instantaneous reproduction number R(t) often assume static and/or homogeneously mixing populations and cannot describe how individuals generate and acquire infections heterogeneously based on their movement patterns within a day. Renewal equations underpin many such popular estimators of R(t), and here, we develop a network-based modelling framework from which we derive new mechanism-led renewal equations and control indicators for outbreaks of infectious diseases. These equations directly integrate within-day human movement to rigorously define a family of instantaneous reproduction numbers; inward, outward, and type R(t) for individual locations, R(t) between locations, R(t) at meeting locations, and R(t) for the entire mobility network. These quantities correct for the unsuitability of existing location-specific R(t) estimators that operate in closed, static populations. Applying our framework to epidemics on diverse types of networks alongside mobile phone data, we demonstrate how our new framework's outputs provide new, multi-scale control indicators at the network, location, and transmission corridor scales, and can be used to design targeted disease control interventions including the strength, type, and length of intervention required across space and time. We capture the biasing effects of different existing ways to measure location-specific and network-level transmission potential without capturing within-day human movements. This generalisable framework redefines reproduction numbers in real-world outbreaks that are shaped by individuals moving across connected locations, enabling more spatially and temporally precise interventions.
