论文标题
基于模型的评估非限制人群中病毒传播风险
Model-based assessment of the risks of viral transmission in non-confined crowds
论文作者
论文摘要
这项工作旨在评估Covid-19疾病在各种日常生活情况下传播的风险（称为场景），涉及大量无言行行人，主要是户外。更具体地说，我们开发了一种从行人的斑点随身携带中推断出全球新感染数量的方法。该方法依赖于疾病传播病毒呼吸液滴的典范模型，这些模型符合有关COVID-19的现有暴露研究。该方法应用于我们在大流行期间获得的有关行人轨迹和方向的详细现场数据。这使我们能够按照他们所存在的感染风险进行对调查的方案进行排名； importantly, the obtained hierarchy of risks is conserved across all our transmission models (except the most pessimistic ones): Street caf{é}s present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets (average density around 0.1 person/m${}^2$) are comparatively quite 低的。尽管我们的临时模型都无法声称准确性，但它们的收敛预测为这些发现提供了信誉。}在有移动人群的情况下，我们发现密度是影响估计感染率的主要因素。最后，我们的研究探讨了街道和场地重新设计在减轻病毒蔓延方面的效率：虽然在（宽）人行道中执行单向人口流量的好处尚不清楚，但改变了队列的几何形状，会影响疾病的传播风险。
This work aims to assess the risks of Covid-19 disease spread in diverse daily-life situations (referred to as scenarios) involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchyobservations of pedestrians. The method relies on ad hoc spatially resolved models for disease transmissionvia virus-laden respiratory droplets, which are fit to existing exposure studies about Covid-19. The approach is applied to the detailed field data about pedestrian trajectories and orientations that we acquired during the pandemic. This allows us to rank the investigated scenarios by the infection risks that they present; importantly, the obtained hierarchy of risks is conserved across all our transmission models (except the most pessimistic ones): Street caf{é}s present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets (average density around 0.1 person/m${}^2$) are comparatively quite low. While none of our ad hoc models can claim accuracy, their converging predictions lend credence to these findings.} In scenarios with a moving crowd, we find that density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.
