Quantifying protocols for safe school activities

PLoS One. 2022 Sep 14;17(9):e0273425. doi: 10.1371/journal.pone.0273425. eCollection 2022.

Abstract

By the peak of COVID-19 restrictions on April 8, 2020, up to 1.5 billion students across 188 countries were affected by the suspension of physical attendance in schools. Schools were among the first services to reopen as vaccination campaigns advanced. With the emergence of new variants and infection waves, the question now is to find safe protocols for the continuation of school activities. We need to understand how reliable these protocols are under different levels of vaccination coverage, as many countries have a meager fraction of their population vaccinated, including Uganda where the coverage is about 8%. We investigate the impact of face-to-face classes under different protocols and quantify the surplus number of infected individuals in a city. Using the infection transmission when schools were closed as a baseline, we assess the impact of physical school attendance in classrooms with poor air circulation. We find that (i) resuming school activities with people only wearing low-quality masks leads to a near fivefold city-wide increase in the number of cases even if all staff is vaccinated, (ii) resuming activities with students wearing good-quality masks and staff wearing N95s leads to about a threefold increase, (iii) combining high-quality masks and active monitoring, activities may be carried out safely even with low vaccination coverage. These results highlight the effectiveness of good mask-wearing. Compared to ICU costs, high-quality masks are inexpensive and can help curb the spreading. Classes can be carried out safely, provided the correct set of measures are implemented.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • COVID-19* / epidemiology
  • COVID-19* / prevention & control
  • Humans
  • Immunization Programs
  • Schools
  • Students
  • Vaccination Coverage

Grants and funding

This work was supported by the Center for Research in Mathematics Applied to Industry (FAPESP grants 2013/07375-0), by the Royal Society London, by the Brazilian National Council for Scientific and Technological Development (CNPq; grants 301778/2017-5, 302836/2018-7, 304301/2019-1, 306090/2019-0, 403679/2020-6) and by the Serrapilheira Institute (Grant No. Serra-1709-16124). JG was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) grant 88887.685473/2022-00. GTG has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 765048. ERS was supported by FAPESP grant 2018/10349-4. KO and SHAL acknowledge the project promat-maragogi and CJS acknowledges the financial support of CNPq and FAPERJ, DM received funding from the grants CNPq-306566/2019-2 and FAPERJ - E-26/202.764/2017. Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0).