The transmission of respiratory diseases such as COVID-19 is exacerbated in densely populated urban areas and crowded indoor settings. Despite the majority of transmissions occurring in such settings, controlling viral spread through individual-level contacts indoors remains challenging. Experimental studies have investigated the transmission patterns of respiratory behaviors such as coughing or sneezing in controlled spatial environments. However, the effects of dynamic movement and spatial structures have been ignored, making it difficult to apply findings to urban policy and planning. To address this gap, we developed agent-based simulations to investigate individual virus inhalation patterns across multiple scenarios in a symmetrical and formulaic indoor space. We conducted sensitivity analysis using regression emulator models to identify significant factors for viral transmission. Our results indicate positive associations with viral transmission in descending order of: (1) stay time; (2) encounter frequency; and (3) initial infected population; while negative associations are: (4) mask wearing; (5) distance to infected people; (6) nearest infected people's mask wearing; and (7) distance to entrance. We also found that narrow passages between obstacles increase virus transmission from breathing. Furthermore, we conducted a case study to investigate the potential of reducing the amount of individually inhaled virus by controlling behaviors and spatial environments. Our findings suggest that mask wearing and reduced stay time can substantially reduce transmission risk, while a large number of contacts and high grouping time result in the growth of the infected population at a certain threshold. These results provide guidance for decision makers to formulate guidelines for curbing the spread of respiratory diseases in indoor spaces.

中文翻译：

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通过基于主体的建模研究呼吸道疾病室内传播的因素

在人口稠密的城市地区和拥挤的室内环境中，COVID-19 等呼吸道疾病的传播会加剧。尽管大多数传播发生在此类环境中，但通过室内个人接触控制病毒传播仍然具有挑战性。实验研究调查了受控空间环境中咳嗽或打喷嚏等呼吸行为的传播模式。然而，动态运动和空间结构的影响被忽视，使得研究结果难以应用于城市政策和规划。为了解决这一差距，我们开发了基于代理的模拟，以研究对称和公式化的室内空间中多种场景下的个体病毒吸入模式。我们使用回归模拟器模型进行敏感性分析，以确定病毒传播的重要因素。我们的结果表明，与病毒传播呈正相关，按降序排列：（1）停留时间；(2) 遭遇频率；(3) 初始感染人群；负面关联是：（4）戴口罩；（五）与感染者的距离；（六）最近的感染者是否佩戴口罩；(7) 距入口的距离。我们还发现，障碍物之间的狭窄通道会增加病毒通过呼吸的传播。此外，我们还进行了一项案例研究，以调查通过控制行为和空间环境来减少个人吸入病毒数量的潜力。我们的研究结果表明，佩戴口罩和减少停留时间可以大大降低传播风险，而接触次数多、聚集时间长，导致感染人群增长到一定阈值。这些结果为决策者制定遏制呼吸道疾病在室内空间传播的指南提供了指导。