Department of Computer Science and Mathematics, Munich University of Applied Sciences HM, Munich, Germany.
Department of Informatics, Technical University of Munich, Garching, Germany.
PLoS One. 2022 Aug 30;17(8):e0273820. doi: 10.1371/journal.pone.0273820. eCollection 2022.
The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a 'high risk' benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.
冠状病毒病（COVID-19）大流行改变了我们的生活，仍然对科学构成挑战。许多研究有助于更好地了解大流行。特别是，吸入气溶胶化的病原体已被确定为传播的必要条件。这些信息对于减缓传播至关重要，但在日常情况下个体感染的可能性仍然不确定。数学模型有助于估计这种风险。在这项研究中，我们提出了如何在局部范围内对 SARS-CoV-2 的空气传播进行建模。在这方面，我们将微观人群模拟与新的疾病传播模型相结合。受房室模型的启发，我们将虚拟人描述为感染或易感。感染的人会呼出与持续气溶胶结合的病原体，而易感的人在穿过感染人留下的气溶胶云时会吸收病原体。传播取决于气溶胶云的病原体负荷，它会随时间变化。我们提出了一个“高风险”基准场景，以区分关键情况和非关键情况。队列的参数研究表明，新模型适合定性评估暴露风险，从而使科学家或决策者能够更好地评估 COVID-19 和类似疾病的传播。
