用于房间级空气传播流行病扩散的高分辨率时空模型
High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread
AI总结：
针对单房间尺度空气传播流行病建模不足的问题，提出融合LiDAR三维数据、CFD气流模拟与SEI流行病学模型的高分辨率时空方法，并揭示房间拓扑和人员分布对通风减排效果的关键影响。
摘要：
空气传播流行病在人类历史上已造成全球数百万人死亡、大规模经济损失以及灾难性的社会学变迁。研究人员已经开发了多种数学模型和计算框架，以在国家、城市、大型社交活动乃至建筑物等不同层级和尺度上研究并预测流行病传播。然而，在最小尺度，即单个房间内，对空气传播流行病动力学进行建模的尝试大多被忽视。随着全球城市化进程导致室内停留时间增加，更多感染发生在共享房间中。本研究提出了一种结合气流动力学的高分辨率时空流行病学模型，用于评估空气传播流行病的扩散。该模型利用通过光探测与测距（LiDAR）设备获取的高分辨率3D数据进行实现，并基于用于气流的计算流体动力学（CFD）模型和用于流行病学动力学的易感-暴露-感染（SEI）模型来计算该模型。研究在四种类型的房间中评估流行病传播，结果表明即使暴露持续时间很短，也存在显著差异。我们表明，房间的拓扑结构以及个体在房间内的分布决定了通风通过呼吸区感染来降低流行病传播的能力。
英文摘要：
Airborne pandemics have caused millions of deaths worldwide, large-scale economic losses, and catastrophic sociological shifts in human history. Researchers have developed multiple mathematical models and computational frameworks to investigate and predict the pandemic spread on various levels and scales such as countries, cities, large social events, and even buildings. However, modeling attempts of airborne pandemic dynamics on the smallest scale, a single room, have been mostly neglected. As time indoors increases due to global urbanization processes, more infections occur in shared rooms. In this study, a high-resolution spatio-temporal epidemiological model with airflow dynamics to evaluate airborne pandemic spread is proposed. The model is implemented using high-resolution 3D data obtained using a light detection and ranging (LiDAR) device and computing the model based on the Computational Fluid Dynamics (CFD) model for the airflow and the Susceptible-Exposed-Infected (SEI) model for the epidemiological dynamics. The pandemic spread is evaluated in four types of rooms, showing significant differences even for a short exposure duration. We show that the room's topology and individual distribution in the room define the ability of air ventilation to reduce pandemic spread throughout breathing zone infection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
