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The risk of long range, herein `airborne', infection needs to be betterunderstood and is especially urgent during the current COVID-19 pandemic. Wepresent a method to determine the relative risk of airborne transmission thatcan be readily deployed with either modelled or monitored CO$_2$ data andoccupancy 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 schoolclassroom, we establish protocols to assess the absolute risk of airborneinfection of this regular attendance at work or school. We present amethodology to easily calculate the expected number of secondary infectionsarising from a regular attendee becoming infectious and remainingpre/asymptomatic within these spaces. We demonstrate our model by calculatingrisks for both a modelled open-plan office and by using monitored data recordedwithin a small naturally ventilated office. In addition, by inferringventilation rates from monitored CO$_2$ we show that estimates of airborneinfection can be accurately reconstructed; thereby offering scope for moreinformed retrospective modelling should outbreaks occur in spaces where CO$_2$is monitored. Our modelling suggests that regular attendance at an office forwork is unlikely to significantly contribute to the pandemic but only ifrelatively quiet desk-based work is carried out in the presence of adequateventilation (i.e. at least 10\,l/s/p following UK guidance), appropriatehygiene controls, distancing measures, and that all commuting presents minimalinfection risk. Crucially, modelling even moderate changes to the conditionswithin the office, or basing estimates for the infectivity of the SARS-CoV-2variant B1.1.7 current data, typically results in the prediction that for asingle infector within the office the airborne route alone gives rises to morethan one secondary infection.

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