Abstract
Strategies adopted globally to mitigate the threat of COVID–19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID–19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID–19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant “what if / instead of” lockdown counterfactuals. Uncertainty quantification for the predictive approaches is described. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model.
Highlights
The global race to manage the existential threat posed by COVID–19 has used non-pharmaceutical interventions (NPIs) such as lockdowns measures as a central tenet
Where the parameters governing dynamics of models cannot be estimated due to data sparsity, we use recent results published in the COVID–19 literature on infection dynamics as well as expert opinion from the Irish Epidemiological Modelling Advisory Group (IEMAG); note that IEMAG developed an initial Susceptible Exposed Infectious Removed (SEIR) model [13] that we extend through the introduction of age-structuring and incorporation of the contact patterns, relaxing the assumption of homogeneous mixing across population age groups
Based on the SEIR model described in this article, we have developed an app that, given a user specified lockdown regime, creates an 8 week ahead forecast for estimated deaths and economic costs in Dublin
Summary
The global race to manage the existential threat posed by COVID–19 has used non-pharmaceutical interventions (NPIs) such as lockdowns (or restriction of movement) measures as a central tenet. Where the parameters governing dynamics of models cannot be estimated due to data sparsity, we use recent results published in the COVID–19 literature on infection dynamics as well as expert opinion from the Irish Epidemiological Modelling Advisory Group (IEMAG); note that IEMAG developed an initial SEIR model [13] (see [Gleeson et al, in press]) that we extend through the introduction of age-structuring and incorporation of the contact patterns, relaxing the assumption of homogeneous mixing across population age groups.
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