Predictive and retrospective modelling of airborne infection risk using monitored carbon dioxide

Henry C Burridge,P F Linden,Shiwei Fan,Roderic L Jones,Catherine J Noakes

doi:10.1177/1420326x211043564

Henry C Burridge, P F Linden + Show 3 more

Open Access

https://doi.org/10.1177/1420326x211043564

Copy DOI

Abstract

The risk of long range, herein ‘airborne', infection needs to be better understood and is especially urgent during the COVID-19 pandemic. We present a method to determine the relative risk of airborne transmission that can be readily deployed with either modelled or monitored CO2 data and occupancy levels within an indoor space. For spaces regularly, or consistently, occupied by the same group of people, e.g. an open-plan office or a school classroom, we establish protocols to assess the absolute risk of airborne infection of this regular attendance at work or school. We present a methodology to easily calculate the expected number of secondary infections arising from a regular attendee becoming infectious and remaining pre/asymptomatic within these spaces. We demonstrate our model by calculating risks for both a modelled open-plan office and by using monitored data recorded within a small naturally ventilated office. In addition, by inferring ventilation rates from monitored CO2, we show that estimates of airborne infection can be accurately reconstructed, thereby offering scope for more informed retrospective modelling should outbreaks occur in spaces where CO2 is monitored. Well-ventilated spaces appear unlikely to contribute significantly to airborne infection. However, even moderate changes to the conditions within the office, or new variants of the disease, typically result in more troubling predictions.

Highlights

The coronavirus disease COVID-19, which causes respiratory symptoms, was declared a pandemic by the World Health Organization (WHO) on the 11 March 2020 – thereby marking its global impact
We focus on assessing the risk of infection of respiratory diseases via the airborne route, taking COVID-19 as an example and, deriving a methodology for calculating the expected number of secondary infections that might arise within any indoor space that is regularly attended by the same group of people, applicable to any airborne disease
The likelihood of spread within the vast majority of indoor spaces, even over periods of a few hours, is reasonably low

Summary

Introduction

The coronavirus disease COVID-19, which causes respiratory symptoms, was declared a pandemic by the World Health Organization (WHO) on the 11 March 2020 – thereby marking its global impact. It was chosen to report the model in a form that can only be applied to indoor spaces for which the air is relatively well mixed and the flows are in steady-state Under these restrictive assumptions, the classical Wells-Riley equation is recovered, namely that the likelihood, P, that infection spreads within a given indoor space during a time interval T is. The empirical data (underlying the estimates of quanta generation rates) implicitly accounts for some of the differing physics expected to arise between the transport of hypothesised gaseous infectious air (required by Wells-Riley based models) and the actual transport of infectious particles – which for airborne infection to occur must (by the definition of the transmission route) be able to be ‘suspended in air over long distances and time’. Rudnick and Milton[13] chose to express their result as equation (4)

À exp À I q fT

Findings

Discussion and conclusions