Peer-Reviewed Journal Details
Mandatory Fields
Gleeson, JP;Murphy, TB;O'Brien, JD;Friel, N;Bargary, N;O'Sullivan, DJP
2022
January
Philosophical Transactions: Mathematical, Physical And Engineering Sciences
Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates
Published
0 ()
Optional Fields
R PACKAGE
380
We describe the population-based susceptible-exposed-infected-removed (SEIR) 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 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. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
LONDON
1364-503X
10.1098/rsta.2021.0120
Grant Details