The SARS-CoV-2 epidemic continues to have major impacts on children’s education, with schools required to implement infection control measures that have led to long periods of absence and classroom closures. We have developed an agent-based epidemiological model of SARS-CoV-2 transmission that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties; the basis of our approach is a contact model constructed using random networks, informed by structured expert judgment. The effectiveness of mitigation strategies is considered in terms of effectiveness at suppressing infection outbreaks and limiting pupil absence. Covid-19 infections in schools in the UK in Autumn 2020 are re-examined and the model used for forecasting infection levels in autumn 2021, as the more infectious Delta-variant was emerging and school transmission was thought likely to play a major role in an incipient new wave of the epidemic. Our results are in good agreement with available data and indicate that testing-based surveillance of infections in the classroom population with isolation of positive cases is a more effective mitigation measure than bubble quarantine both for reducing transmission in primary schools and for avoiding pupil absence, even accounting for the insensitivity of self-administered tests. Bubble quarantine entails large numbers of pupils being absent from school, with only a modest impact on classroom infection levels. However, maintaining a reduced contact rate within the classroom can have a major beneficial impact on managing Covid-19 in school settings.

An agent-based stochastic model of SARS-CoV-2 transmission in classrooms, developed in Fortran for fast ensemble simulation.

The CoMMinS classroom model is written in Fortran, with links to Python for file reading and generation of contact network diagrams.

The code uses:
  - Fortran GSL libraries for random sampling from distributions (https://doku.lrz.de/display/PUBLIC/FGSL+-+A+Fortran+interface+to+the+GNU+Scientific+Library)
  - Fortran NetCDF for efficient storage of results (https://docs.unidata.ucar.edu/netcdf-fortran/current/)
  - Fortran--Python bindings are provided by ForpY (https://github.com/ylikx/forpy)
  - some modules from https://leonfoks.github.io/coretran/index.html

Note, the Python code is stored in a separate directory provided as an environment variable called "COVID_SRC" after compilation.

Requirements:
  - Gcc version >=9.4.0
  - Python >= 3.9
  - Netcdf fortran library
  - Fortran gsl library