Scientists develop model to assess COVID-19 infection risk in offices and schools

As more UK workers and students return to offices and schools, a new model has been developed to predict the risk of airborne COVID-19 infection in such environments.

  People in office sitting in front of computers  Credit: Israel Amdrade via Unsplash

The model – developed by researchers at the University of Cambridge, Imperial College London, and the University of Leeds – uses monitored CO2 and occupancy data to predict how many workers are likely to be infected by an asymptomatic but infectious colleague.

Applications of the infection model have demonstrated that most workers in well-ventilated, quiet offices are unlikely to infect each other via airborne particles, but the risk becomes greater if the space is poorly ventilated or if the workers are involved in activities that require more speaking. For instance, the model predicts each infected person could infect two to four others in an adequately ventilated but noisy call centre. Risks are also likely to increase if the infected individual is a ‘super spreader’.

The model also suggests that halving the occupancy of an office could reduce the risk of airborne transmission four-fold. The results are reported in the journal Indoor and Built Environment.

In areas with lower ventilation rates and high occupancy, CO2 levels are higher, so monitoring them can provide a warning to building managers to identify areas where the risk of airborne transmission of COVID-19 are higher. Achievable interventions can then be made, for instance, to improve ventilation or change worker attendance patterns to reduce occupancy.

In shared spaces such as offices and classrooms, exposure to infectious airborne matter builds up, and room occupancy may vary. By using carbon dioxide levels as a proxy for exhaled breath, the model can assess the variable exposure risk as people come and go.

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Image: People in office sitting in front of computers

Credit: Israel Amdrade via Unsplash

Reproduced courtesy of the University of Cambridge

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