Pupils returning to primary schools in England during 2020: rapid estimations of punctual COVID-19 infection rates

Development of a high-speed bioaerosol elimination system for treatment of indoor air

We developed a high-speed filterless airflow multistage photocatalytic elbow aerosol removal system for the treatment of bioaerosols such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human-generated bioaerosols that diffuse into indoor spaces are 1-10 μm in size, and their selective and rapid treatment can reduce the risk of SARS-CoV-2 infection. A high-speed airflow is necessary to treat large volumes of indoor air over a short period. The proposed system can be used to eliminate viruses in aerosols by forcibly depositing aerosols in a high-speed airflow onto a photocatalyst placed inside the system through inertial force and turbulent diffusion. Because the main component of the deposited bioaerosol is water, it evaporates after colliding with the photocatalyst, and the nonvolatile virus remains on the photocatalytic channel wall. The residual virus on the photocatalytic channel wall is mineralized via photocatalytic oxidation with UVA-LED irradiation in the channel. When this system was operated in a 4.5 m³ aerosol chamber, over 99.8% aerosols in the size range of 1-10 μm were removed within 15 min. The system continued delivering such performance with the continuous introduction of aerosols. Because this system exhibits excellent aerosol removal ability at a flow velocity of 5 m/s or higher, it is more suitable than other reactive air purification systems for treating large-volume spaces.

Alternative COVID-19 mitigation measures in school classrooms: analysis using an agent-based model of SARS-CoV-2 transmission

The SARS-CoV-2 epidemic has impacted children's education, with schools required to implement infection control measures that have led to periods of absence and classroom closures. We developed an agent-based epidemiological model of SARS-CoV-2 transmission in a school classroom that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties. Our approach is based on a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies in suppressing infection outbreaks and limiting pupil absence are considered. COVID-19 infections in primary schools in England in autumn 2020 were re-examined and the model was then used to estimate infection levels in autumn 2021, as the Delta variant was emerging and it was thought likely that school transmission would play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance is more effective than bubble quarantine, both for reducing transmission and avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of absences, with only modest impact on classroom infections. However, maintaining reduced contact rates within the classroom can have a major benefit for managing COVID-19 in school settings.

Pupils returning to primary schools in England during 2020: rapid estimations of punctual COVID-19 infection rates

Drawing on risk methods from volcano crises, we developed a rapid COVID-19 infection model for the partial return of pupils to primary schools in England in June and July 2020, and a full return in September 2020. The model handles uncertainties in key parameters, using a stochastic re-sampling technique, allowing us to evaluate infection levels as a function of COVID-19 prevalence and projected pupil and staff headcounts. Assuming average national adult prevalence, for the first scenario (as at 1 June 2020) we found that between 178 and 924 [90% CI] schools would have at least one infected individual, out of 16 769 primary schools in total. For the second return (July), our estimate ranged between 336 (2%) and 1873 (11%) infected schools. For a full return in September 2020, our projected range was 661 (4%) to 3310 (20%) infected schools, assuming the same prevalence as for 5 June. If national prevalence fell to one-quarter of that, the projected September range would decrease to between 381 (2%) and 900 (5%) schools but would increase to between 2131 (13%) and 9743 (58%) schools if prevalence increased to 4× June level. When regional variations in prevalence and school size distribution were included in the model, a slight decrease in the projected number of infected schools was indicated, but uncertainty on estimates increased markedly. The latter model variant indicated that 82% of infected schools would be in areas where prevalence exceeded the national average and the probability of multiple infected persons in a school would be higher in such areas. Post hoc, our model projections for 1 September 2020 were seen to have been realistic and reasonable (in terms of related uncertainties) when data on schools' infections were released by official agencies following the start of the 2020/2021 academic year.