An age-cohort simulation model for generating COVID-19 scenarios: A study from Ireland's pandemic response

Jim Duggan, Jair Andrade, Thomas Brendan Murphy, James P. Gleeson, Cathal Walsh and Philip Nolan

European Journal of Operational Research, 2024, vol. 313, issue 1, 343-358

Abstract: The COVID-19 pandemic presented an immediate need for the Irish Government to establish modelling capacity in order to inform public health decision making. A broad-based interdisciplinary team was created at short notice, drawing together related expertise from the academic and health sectors. This paper documents one of a number of modelling solutions developed by the Irish Epidemiological Modelling Advisory Group (IEMAG), which advised the Irish Government on COVID-19 responses during the pandemic. The model inputs included surveillance data, epidemiological data, demographic data, vaccination schedules, vaccine efficacy estimates, estimates of social contacts, and new variant data. Outputs from the model supported policy discussions, including: decisions on the timing of public health restrictions, simulating the effects of school reopening on overall disease transmission, exploring the impact of vaccination across different age cohorts, and generating scenarios on the plausible impact on cases caused by the Omicron variant. An innovative aspect of the solution was the use of a modular design, with three benefits: (1) it enabled a simplification of the disease transmission structure; (2) it provided a practical workflow to coordinate activities; and (3) it speeded up the process of scenario generation and the requirement to provide timely and informative scenario analysis to support Ireland's pandemic response. Given the paper's applied and practical focus, it presents a record of modelling and scenario outputs as they were developed, presented and deployed during the actual outbreak - therefore all simulation results and scenarios are documented “as they happened”, and without the benefit of hindsight.

Keywords: System dynamics; OR in government; OR in health services; Decision support systems (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:313:y:2024:i:1:p:343-358

DOI: 10.1016/j.ejor.2023.08.011

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