doi: https://doi.org/10.5281/zenodo.4541431
We investigate the impact of reducing school and other (non-school-related) contacts on COVID-19 control using an age-structured model fitted to age-specific seroprevalence and hospital admission data from the Netherlands.
The analyses were published as
Rozhnova G, van Dorp CH, Bruijning-Verhagen P, Bootsma MCJ, van de Wijgert JHHM, Bonten MJM, Kretzschmar ME. Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic. Nature Communications. 2021;12(1):1614. https://doi.org/10.1038/s41467-021-21899-6.
The data are added to the data folder for convenience.
We digitized data from the publication
Vos ERA, den Hartog G, Schepp RM, et al Nationwide seroprevalence of SARS-CoV-2 and identification of risk factors in the general population of the Netherlands during the first epidemic wave J Epidemiol Community Health Published Online First: 28 November 2020. doi: https://doi.org/10.1136/jech-2020-215678
We used contact patterns from the preprint
Jantien A. Backer, Liesbeth Mollema, R.A. Eric Vos, Don Klinkenberg, Fiona R.M. van der Klis, Hester E. de Melker, Susan van den Hof, Jacco Wallinga The impact of physical distancing measures against COVID-19 transmission on contacts and mixing patterns in the Netherlands: repeated cross-sectional surveys in 2016/2017, April 2020 and June 2020 medRxiv 2020.05.18.20101501; doi: https://doi.org/10.1101/2020.05.18.20101501
The school-specific contact matrix was taken from the publication
Prem K, Cook AR, Jit M. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLOS Computational Biology 2017;13(9):1-21. doi: https://doi.org/10.1371/journal.pcbi.1005697
We used publicly available data from the Statistics Netherlands (CBS): https://www.cbs.nl.
The hospital data included COVID-19 hospitalizations by date of admission and stratified by age in the Netherlands (OSIRIS database).
Model inference was done with R Version 3.6.0 using R Studio Version 1.3.959 (Interface to R) and Stan using rstan R package Version 2.19.3 (R interface to Stan) and cmdstanr R package Version 0.1.3 on Windows 10 Home Version 1903. The scripts can be found in the scripts directory. The R and Stan scripts are based on scripts used for the publication
van Boven M, Teirlinck AC, Meijer A, Hooiveld M, van Dorp CH, Reeves RM, Campbell H, van der Hoek W; RESCEU Investigators. Estimating Transmission Parameters for Respiratory Syncytial Virus and Predicting the Impact of Maternal and Pediatric Vaccination. J Infect Dis. 2020 Oct 7;222(Supplement_7):S688-S694. doi: https://doi.org/10.1093/infdis/jiaa424
Analysis of the model was performed using Mathematica Version Number 10.0.2.0 on Platform Mac OS X El Capitan Version 10.11.5. The notebook SchoolAnalyses.nb can be found in the notebooks directory.
Figures for the manuscript were produced in the notebook SchoolAnalyses.nb. Figures can be found in the figures directory.
Notebook JointPosteriorPlot.ipynb produces Figure S5 of the manuscript (posterior correlations between the parameters).
Output files produced in R or Mathematica can be found in the output directory.
Mac OS X El Capitan Version 10.11.5 for Mathematica notebook
Windows 10 Home Version 1903 for R and Stant scripts
Our study requires only a standard computer with enough RAM to support the in-memory operations.
R Version 3.6.0 https://www.r-project.org/
R Studio Version 1.3.959 (Interface to R) https://rstudio.com/
rstan R package Version 2.19.3 (R interface to Stan) https://cran.r-project.org/web/packages/rstan/vignettes/rstan.html
cmdstanr R package Version 0.1.3 on Windows 10 Home Version 1903 https://mc-stan.org/cmdstanr/
Mathematica 10.0.2.0 https://www.wolfram.com/mathematica/