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

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.

Understanding the role of mask-wearing during COVID-19 on the island of Ireland

“…Cazelles et al [44] used a stochastic model with time-varying parameters and studied the prevalence and tracked the effective reproductive number and its percentage reduction between restriction periods during the first year of the epidemic. Jaouimaa et al [45] incorporated an age-structured contact matrix to their compartmental model and explored the evaluation of an age-related economic cost of different lockdown measures in the ROI. Several UK studies also included NI data to understand regional COVID-19 dynamics [46, 47].…”

“…Jaouimaa et al [45] incorporated an age-structured contact matrix to their compartmental model and explored the evaluation of an age-related economic cost of different lockdown measures in the ROI. Several UK studies also included NI data to understand regional COVID-19 dynamics [46,47].…”

Estimating time-dependent infectious contact: a multi-strain epidemiological model of SARS-CoV-2 on the island of Ireland

“…In the Irish context, compartmental models have been used by several other research groups to understand the dynamics of the virus, make forecasts of outcomes under various scenarios, and assess economic impacts of policy restrictions [18][19][20][21][22][23].…”

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

“…Secondly, the 2017–2018 BBC Pandemic study [ 19 ] recruited 36 000 volunteers from the UK who used a self-reporting mobile phone app to inform about contacts made. Based on these surveys each study published (pre-pandemic) estimates for the so-called ‘contact matrix’ C 0 , which captures the social mixing patterns between age groups [ 20 ] that have been widely used to model transmission dynamics of COVID-19. In this paper we infer the time-varying reproduction number R t [ 21 , 22 ] to quantify the net impact of societal response (lockdowns, social distancing, etc.)…”