Abstract
The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, R0, influence the simulated outbreaks, and find that our model behaves as expected: the higher the R0, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases.
Highlights
While many models focus on estimating disease parameters, such as the basic reproductive number or the length of time between exposure and when an individual becomes infectious, here we focus on those models created to simulate the spread of the disease through a population, so as to get a better understanding of how COVID-19 will affect a particular country, and the likely outcomes of different interventions
Because agents are infectious before showing symptoms and do not adjust their actions in the exposed state, these infectious but presymptomatic agents will continue to go to school, bringing the disease to school where they have a high number of contacts with other students who will become infected and bring the disease home to spread to their family and others in their social networks
This adaptability is an important characteristic of agent-based models for epidemiology, which means that creating and maintaining an agent-based model of a society and region is a useful investment as part of pandemic preparedness, because such a baseline model can be adapted to the specific characteristics of different diseases relatively quickly
Summary
As the COVID-19 pandemic continues, countries throughout the world are attempting to find the best strategies to slow the spread of the disease so that it does not overwhelm the health care systems. Epidemiological modelling can play an important role in developing and implementing successful intervention strategies, but in many cases, an appropriate model needs to be developed. We demonstrate the feasibility of taking a previously developed and validated model for the spread of infectious diseases in Ireland and adjusting the parameters of the disease components to model the spread of COVID-19. COVID-19 is known to be more severe in older patients and those with co-morbidities [1]
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