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ABSTRACT HEADING

The COVID-19 pandemic has led to a dramatic loss of human activities worldwide. Its impact on society becomes more significant and dreadful. An unprecedented challenge is presented not only to the health system but also to the educational system. Maintaining students' learning performance while keeping their health during the crisis has gained attention from governors, educators, parents, etc. In this paper, first, monitored information about two classrooms during a typical day of the crisis was presented. The occupants' information (students' age, number, activities), ventilation system status, window status, and indoor CO₂ concentration are included. Then a transient CO₂ concentration model was introduced. Sensitivity analysis was conducted to identify the importance rank of model inputs and parameters. Finally, the measurements were employed to calibrate the CO₂ concentration model with Bayesian inference. The calibrated model parameters, such as ventilation rate, could be used to estimate its impact on COVID-19 infection risk.