AI 中文总结
该研究通过含100万智能体的ABM，发现流动假设会显著改变接触网络结构与疫情轨迹，强调流动模型校准对疫情模拟及干预评估的重要性。
AI 中文摘要
人类流动在塑造驱动传染病传播的接触模式中发挥核心作用，但由于数据和计算限制，基于智能体的模型（ABMs）中通常对流动进行简化，这些简化对模型输出的影响尚不明确。本研究系统探究了不同流动假设如何在大规模ABM中影响涌现的接触网络与疫情动态：使用代表城市环境的100万智能体的合成种群，实现沿两个维度变化的5种流动模型——活动模式（经验推导vs.随机化）与目的地选择机制（经验流行度、距离相关或随机）。在疾病参数保持不变的情况下，仅流动假设就产生了显著不同的接触网络结构与疫情轨迹，包括峰值发病率的差异和疫情时间的偏移；重要的是，这些差异不能归因于智能体总体移动量的多少，因为各模型的总移动量大致相当，相反，对比动态源于流动生成接触机会的方式：即谁在何地、多久见谁。这些结果表明，在未仔细校准流动模型的模型中，模拟的疫情结果与干预措施评估可能既反映了基础疾病参数，也反映了流动假设；本研究强调了流动模型校准与验证的重要性，尤其在面向政策的应用中。
英文摘要
Human mobility plays a central role in shaping contact patterns that drive infectious disease transmission, yet mobility is often simplified in agent-based models (ABMs) due to data and computational constraints. The effects of these simplifications on model outputs are poorly understood. In this study, we systematically examined how alternative mobility assumptions influence emergent contact networks and epidemic dynamics within a large-scale ABM. Using a synthetic population of one million agents representing an urban environment, we implemented five mobility models varying along two dimensions: activity patterns (empirically derived vs. randomized) and destination choice mechanisms (empirical popularity, distance-based, or random). Holding disease parameters constant, we found that mobility assumptions alone produced substantially different contact network structures and epidemic trajectories, including differences in peak incidence and shifts in outbreak timing. Importantly, these differences could not be attributed to agents simply moving more or less overall since aggregate movement volumes were broadly comparable across models. Instead, the contrasting dynamics arose from how mobility generates contact opportunities: specifically, who meets whom, where, and how often. These results suggest that in models where mobility has not been carefully calibrated, simulated epidemic outcomes and evaluations of interventions may reflect mobility assumptions as much as underlying disease parameters. Our findings underscore the importance of mobility model calibration and validation, particularly in policy-facing applications.
