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

TL;DR

This paper presents a robust calibration method for COVID-19 SEIR models with time-varying contact rates, using data inversion, statistical modeling, and spline-fitting to improve prediction accuracy.

Contribution

It introduces a novel calibration approach combining equation inversion and spline-fitting for SEIR models with dynamic contact rates.

Findings

Effective calibration improves model fit to observed data.

Method enhances prediction reliability for COVID-19 scenarios.

Applicable to a wide class of time-varying epidemiological models.

Abstract

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 model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g., to the daily number of confirmed new cases, as the past history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data, to produce a robust methodology for calibration of a wide class of models of this type.