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Evaluating trade-offs between COVID-19 prevention and learning loss: an agent-based simulation analysis.
1Harvard University, Cambridge, MA, USA.
Royal Society Open Science
|April 24, 2025
Summary
Quarantining students reduced COVID-19 infections but caused significant learning loss. Test-to-stay strategies offered similar infection control with less learning disruption, while masks and vaccines improved outcomes without impacting education.
Area of Science:
- Epidemiology
- Public Health
- Educational Policy
Background:
- The COVID-19 pandemic necessitated school-based mitigation strategies.
- Some strategies risked disrupting in-person learning, creating a trade-off between infection control and educational continuity.
Purpose of the Study:
- To model and evaluate the trade-offs between COVID-19 infection prevention and in-person learning loss in a US high school.
- To compare the effectiveness of various mitigation policies in a post-Omicron variant context.
Main Methods:
- Development of an agent-based model simulating COVID-19 transmission in a high school.
- Simulation of mitigation policies including isolation, quarantine, weekly testing, and test-to-stay.
- Analysis of outcomes under varying mask use and booster dose uptake scenarios over an 11-week trimester.
Main Results:
- Full quarantine (5-10 days) reduced infections 5-7 fold but caused ~40% learning days lost.
- Test-to-stay strategies achieved comparable infection reduction with substantially lower learning loss.
- Weekly testing was less effective and incurred greater learning loss than test-to-stay.
- Universal masking and increased vaccination reduced infections without learning loss and enhanced other strategies.
Conclusions:
- Mitigation strategies present a complex balance between public health and education.
- Test-to-stay policies offer a promising approach to minimize learning loss while controlling infections.
- Non-pharmaceutical interventions like masking and vaccination are crucial for synergistic effects and maintaining in-person learning.
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