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Modeling the impact of COVID-19 testing policies in a primary school

By the SIMID research group (UHasselt, UAntwerp)

The SIMID (Simulation Models of Infectious Diseases) team is a highly interdisciplinary research group working on mathematical and statistical models for infectious diseases and their economic evaluation. The main topics of study are:

  • Modeling control and prevention options for endemic infectious diseases

  • Modeling population threats from emerging infectious diseases

  • New methods and data to improve dynamic infectious disease models

  • Further development of data collection tools and infectious disease databases in Flanders

Computational study of COVID-19 testing policies

A highly debated topic during the COVID-19 pandemic has been the role that children, and therefore schools, play in the transmission of SARS-CoV-2. In addition to avoiding negative effects on children’s education and well-being, reducing transmission in schools is important in order to avoid potential spillover of infection to the general community and more vulnerable individuals. Besides empirical studies, mathematical modeling can help to gain insight into whether and how children contribute to transmission.

We developed an individual-based model of SARS-CoV-2 transmission in primary schools, which was used to investigate the impact of different testing policies [1]. Using this model we also showed that when a repetitive screening strategy is in place, meaning that (part of) the school population is tested at regular intervals, the resulting data can be used to evaluate and if necessary adapt mitigation measures in school during the course of an outbreak [2]. Possible future work in which the VSC will be essential is to investigate how the accuracy of outbreak reconstruction depends on data availability, such as the proportion of infected cases for which a whole genome sequence is available.

The resources provided by the VSC infrastructure play a major role in reducing computing time when running our simulation model for many different parameter values. This makes it possible to investigate the impact of a broad range of scenarios, such as different screening intervals, in a repetitive testing policy.

Visit the SIMID consortium website here



[1] Controlling SARS-CoV-2 in schools using repetitive testing strategies, Andrea Torneri, Lander Willem, Vittoria Colizza, et al., eLife, 2022, 11:e75593, doi:10.7554/eLife.75593

[2] Reconstruction of SARS-CoV-2 outbreaks in a primary school using epidemiological and genomic data, Cécile Kremer, Andrea Torneri, Pieter JK Libin, et al., Epidemics, 2023, 44:100701, doi:10.1016/j.epidem.2023.100701


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