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Strategies adopted globally to mitigate the threat of COVID-19 have primarilyinvolved lockdown measures with substantial economic and social costs withvarying degrees of success. Morbidity patterns of COVID-19 variants have astrong association with age, while restrictive lockdown measures haveassociation with negative mental health outcomes in some age groups. Reducedeconomic prospects may also afflict some age cohorts more than others.Motivated by this, we propose a model to describe COVID-19 community spreadincorporating the role of age-specific social interactions. Through a flexibleparameterisation of an age-structured deterministic Susceptible ExposedInfectious Removed (SEIR) model, we provide a means for characterisingdifferent forms of lockdown which may impact specific age groups differently.Social interactions are represented through age group to age group contactmatrices, which can be trained using available data and are thus locallyadapted. This framework is easy to interpret and suitable for describingcounterfactual scenarios, which could assist policy makers with regard tominimising morbidity balanced with the costs of prospective suppressionstrategies. Our work originates from an Irish context and we use diseasemonitoring data from February 29th 2020 to January 31st 2021 gathered by Irishgovernmental agencies. We demonstrate how Irish lockdown scenarios can beconstructed using the proposed model formulation and show results ofretrospective fitting to incidence rates and forward planning with relevant``what if/instead of'' lockdown counterfactuals with uncertaintyquantification. Our formulation is agnostic to a specific locale, in thatlockdown strategies in other regions can be straightforwardly encoded usingthis model. The methods we describe are made publicly available online throughan accessible and easy to use web interface.

An age-structured SEIR model for COVID--19 incidence in Dublin, Ireland with framework for evaluating health intervention cost