Abstract
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’.
Highlights
The Irish Epidemiological Modelling Advisory Group (IEMAG) was established in March 2020 to provide expert advice to Ireland’s Chief Medical Officer and National Public Health Emergency Team on COVID-19 responses
We describe the population-based susceptibleexposed-infected-removed (SEIR) model developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses
We describe the SEIR model used by IEMAG and give a detailed description of the calibration algorithm, an early version of that appeared in the technical report [12]
Summary
The Irish Epidemiological Modelling Advisory Group (IEMAG) was established in March 2020 to provide expert advice to Ireland’s Chief Medical Officer and National Public Health Emergency Team on COVID-19 responses. We follow the direction of Mummert [15], who showed that a time-varying effective contact rate can be found for susceptible-infected-removed (SIR) systems by an exact inversion of the governing differential equations of the model. In extending this concept, we generalize to a range of models and derive conditions on the model structure and on the smoothness of the data-fitting function, which are required for this approach to be successful. The form of equation (2.14) can be exploited to enable calibration of the model to data
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