Predictive and retrospective modelling of airborne infection risk using monitored carbon dioxide
نویسندگان
چکیده
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 determine relative airborne transmission that can readily deployed with either modelled or monitored CO 2 data occupancy levels within an indoor space. For spaces regularly, consistently, occupied by same group people, e.g. open-plan office school classroom, we establish protocols assess absolute this regular attendance at work school. methodology easily calculate expected number secondary infections arising from attendee becoming infectious remaining pre/asymptomatic these spaces. demonstrate our model calculating risks for both using recorded small naturally ventilated office. In addition, inferring ventilation rates , show estimates accurately reconstructed, thereby offering scope more informed retrospective modelling should outbreaks occur in where monitored. Well-ventilated appear unlikely contribute significantly infection. However, even moderate changes conditions office, new variants disease, typically result troubling predictions.
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Introduction Conclusions References
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ژورنال
عنوان ژورنال: Indoor and Built Environment
سال: 2021
ISSN: ['1420-326X', '1423-0070']
DOI: https://doi.org/10.1177/1420326x211043564