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 agents' health status as susceptible, exposed, infectious or recovered. Infectious agents exhale pathogens bound to persistent aerosols, whereas susceptible agents absorb pathogens when moving through an aerosol cloud left by the infectious agent. 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. Simulating indoor situations show that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or even decision-makers to better assess the spread of COVID-19 and similar diseases.
翻译:冠状病毒(COVID-19)流行病改变了我们的生活,仍然对科学构成挑战。许多研究都有助于更好地了解这一流行病。特别是,吸入气溶胶病原体已被确定为传播的关键。这一信息对于减缓传播至关重要,但个人在日常情况下感染的可能性仍然不确定。数学模型有助于估计这种风险。在本研究中,我们建议如何在当地规模上模拟SARS-COV-2的空中传播。在这方面,我们将微型人群模拟与新的疾病传播模式结合起来。在分包模型的启发下,我们将物剂的健康状况描述为易感染、暴露、传染或恢复。传染性制剂将病原体吸入到持久性气溶胶中,而易感性制剂在通过传染剂留下的气溶胶云移动时吸收病原体。传播取决于气溶胶云的病原体负荷,随着时间的推移而变化。我们提出了一个“高风险”基准情景,以区分临界和非危急情况。我们模拟室内状况表明,新模型适合于评估暴露风险的质量,从而使得科学家或甚至是类似的决定者能够更好地评估CO-19疾病传播。