Modelling airborne transmission of SARS-CoV-2 at a local scale

TL;DR

This paper introduces a novel local-scale model combining crowd simulation and disease transmission dynamics to assess airborne SARS-CoV-2 spread, aiding risk evaluation in indoor environments.

Contribution

It develops a new integrated model for airborne transmission that accounts for aerosol pathogen load dynamics and agent health states, enhancing risk assessment capabilities.

Findings

Model effectively distinguishes high-risk scenarios.

Simulations show qualitative assessment of infection risk.

Framework applicable to COVID-19 and similar diseases.

Abstract

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.