Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment

By G. Buonanno, Luca Stabile, Lidia Morawska. Environment International, 2020

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Abstract Airborne transmission is a pathway of contagion that is still not sufficiently investigated despite the evidence in the scientific literature of the role it can play in the context of an epidemic. While the medical research area dedicates efforts to find cures and remedies to counteract the effects of a virus, the engineering area is involved in providing risk assessments in indoor environments by simulating the airborne transmission of the virus during an epidemic. To this end, virus air emission data are needed. Unfortunately, this information is usually available only after the outbreak, based on specific reverse engineering cases. In this work, a novel approach to estimate the viral load emitted by a contagious subject on the basis of the viral load in the mouth, the type of respiratory activity (e.g. breathing, speaking, whispering), respiratory physiological parameters (e.g. inhalation rate), and activity level (e.g. resting, standing, light exercise) is proposed. The results showed that high quanta emission rates (>100 quanta h-1) can be reached by an asymptomatic infectious SARS-CoV-2 subject performing vocalization during light activities (i.e. walking slowly) whereas a symptomatic SARS-CoV-2 subject in resting conditions mostly has a low quanta emission rate (<1 quantum h-1). The findings in terms of quanta emission rates were then adopted in infection risk models to demonstrate its application by evaluating the number of people infected by an asymptomatic SARS-CoV-2 subject in Italian indoor microenvironments before and after the introduction of virus containment measures. The results obtained from the simulations clearly highlight that a key role is played by proper ventilation in containment of the virus in indoor environments.

Subfields: Clinical Psychology, Modeling and Simulation, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/bb0cf47d74067b1ed0859f25dc9ed63fcf830be6

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In Pulmonary and Respiratory Medicine

  1. G. Buonanno
  2. Qingyan Chen
  3. Z. Zhang

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