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We describe the population-based SEIR (susceptible, exposed, infected,removed) model developed by the Irish Epidemiological Modelling Advisory Group(IEMAG), which advises the Irish government on COVID-19 responses. The modelassumes a time-varying effective contact rate (equivalently, a time-varyingreproduction number) to model the effect of non-pharmaceutical interventions. Acrucial technical challenge in applying such models is their accuratecalibration to observed data, e.g., to the daily number of confirmed new cases,as the past history of the disease strongly affects predictions of futurescenarios. We demonstrate an approach based on inversion of the SEIR equationsin conjunction with statistical modelling and spline-fitting of the data, toproduce a robust methodology for calibration of a wide class of models of thistype.

Calibrating COVID-19 SEIR models with time-varying effective contact rates