  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.
