The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime

Abstract

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

Keywords: Building ventilation; COVID-19; Infection control; SARS-CoV-2.

Figure 1.

(a,b) Images show the depth-averaged concentration of a cloud of dye in a laboratory tank.This evolves in time owing to the mixing produced by the repeated motion of a cylinder, representing movement of people in a corridor. In (a), pictures of the tank are shown as captured during an experiment. In (b), false colours are used to represent the dye concentration field in each of these pictures, with red being the maximum concentration and blue showing absence of dye. The white rectangle on each image in (b) illustrates the position of the cylinder at that time. (c) Collapse of the experimental data of the depth-averaged dye concentration to a continuum model of the concentration of the dye along the channel. The y-axis shows the dimensionless concentration, at each time scaled relative to the theoretical value in the centre of the channel at that time, and the x-axis shows the dimensionless distance along the channel, scaled with the predicted diffusive spreading along the channel at each time (after [70]). (Online version in colour.)

Figure 2.

Image showing the mixing of individual clouds of infected aerosol, dyed different shades of red and normalized relative to the initial concentration as seen on the legend. The corridor is 5 m wide (y-axis) and 20 m long (x-axis), with people (blue dots) moving from left to right along the corridor. In this simulation, the along-corridor people spacing is 2 m and they move with speed 1.5 m s⁻¹, while the clouds of aerosol are produced by one person moving down the corridor,so the older cloud at the left-hand end of the corridor is more dispersed than that on the right (calculations by Bhamidipati and Woods based on [70]). (Online version in colour.)

Figure 3.

(a) Time series of the concentration of CO₂ as recorded by three sensors located in a ventilated but empty corridor in the cystic fibrosis ward of the new Royal Papworth Hospital in Cambridge (UK), after the local release of a pulse of CO₂ at one end of the corridor (black lines). The experiment was repeated with one person continuously walkingback and forth along the corridor, and the red lines show the CO₂ concentration obtained in this second experiment [73]. (b) Schematic illustrating the effect of the mixing associated with the wake of a person walking along the corridor. (Online version in colour.)

Figure 4.

The variation in the absolute expected number of secondary infection, S_I, during the period 25 May 2019 to 20 July 2020 determined from monitored CO₂ and the school’s occupancy timetable for a typical classroom within a relatively modern build school (rebuilt in September 2016). The shaded regions correspond to holiday periods. For data recorded during the summer term of 2019 the average was S_I = 0.35, for the autumn term of 2020 the average was S_I = 0.53, and for data recorded during the spring term of 2020 the average was S_I = 0.63 until the lockdown of 20 March.