Abstract
An age-structured SEIR model simulates the propagation of COVID-19 in the population of Northern Ireland. It is used to identify optimal timings of short-term lockdowns that enable long-term pandemic exit strategies by clearing the threshold for herd immunity or achieving time for vaccine development with minimal excess deaths.
Highlights
Epidemiological population models are developed to analyse the characteristics of infectious disease propagation such as the distribution of epidemic sizes [1], to predict the possible course of future epidemics [2], and to determine the efficacy of possible interventions [3]
An age-structured SEIIIR model for Belgium separates the infection into three levels of severity [9], while an SEIRS model allowing for re-infection has been trained on data from Northern Ireland and South Korea [10]
An example is a model for the UK [17] that compares the use of national lockdowns with localized lockdowns triggered by regional levels of intensive care unit (ICU) bed capacity
Summary
Epidemiological population models are developed to analyse the characteristics of infectious disease propagation such as the distribution of epidemic sizes [1], to predict the possible course of future epidemics [2], and to determine the efficacy of possible interventions [3]. Concerns about a possible second wave, combined with the hope of an imminent vaccine led to further high-intensity ‘circuit break’ lockdowns As these need to be maximally effective and as short as possible to minimize further economic disruption, a particular application of compartmental models was to identify optimal use of circuit breaks [21,22], while other work has explored switching strategies between lockdowns and keeping communities open [23]. We calibrate an age-structured SEIIR model to the age distribution of the population of Northern Ireland and simulate the COVID-19 pandemic throughout 2020 and early 2021, computationally fitting parameters such as infection rates. This model is employed to explore hypothetical implementations of lockdowns. We study the impact of the sensitivity of the trigger and the delay until lockdown begins on clinical outcomes such as the overall cumulative deaths from the pandemic
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