MOAI: A methodology for evaluating the impact of indoor airflow in the transmission of COVID-19

TL;DR

This paper introduces MOAI, a methodology that combines questionnaires and modeling to evaluate how indoor airflow impacts COVID-19 transmission risk, aiming to improve epidemiological understanding and contact tracing.

Contribution

It develops a novel risk evaluation model incorporating airflow factors and temporal dynamics, based on synthetic data and literature review, for better transmission risk assessment.

Findings

Airflow significantly influences COVID-19 transmission risk.

The model quantifies risk exposure considering airflow and time.

Synthetic data supports the model's effectiveness.

Abstract

Epidemiology models play a key role in understanding and responding to the COVID-19 pandemic. In order to build those models, scientists need to understand contributing factors and their relative importance. A large strand of literature has identified the importance of airflow to mitigate droplets and far-field aerosol transmission risks. However, the specific factors contributing to higher or lower contamination in various settings have not been clearly defined and quantified. As part of the MOAI project (https://moaiapp.com), we are developing a privacy-preserving test and trace app to enable infection cluster investigators to get in touch with patients without having to know their identity. This approach allows involving users in the fight against the pandemic by contributing additional information in the form of anonymous research questionnaires. We first describe how the questionnaire was designed, and the synthetic data was generated based on a review we carried out on the latest available literature. We then present a model to evaluate the risk exposition of a user for a given setting. We finally propose a temporal addition to the model to evaluate the risk exposure over time for a given user.