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, $R_0$, influence the simulated outbreaks, and find that our model behaves as expected: the higher the $R_0$, 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.

中文翻译：

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使基于代理的爱尔兰县传染病传播模型适应 COVID-19

导致传染病在人群中传播的动态可以被描述为一个复杂的系统。为这种系统建模的一种方法是基于代理的流行病学建模，以提高防范能力，并更多地了解 COVID-19 等传染病如何在人群中传播。当大流行是由一种新出现的疾病引起时，需要时间来开发一个全新的模型来捕捉系统的复杂性。在本文中，我们讨论了采用现有的基于代理的模型来模拟爱尔兰麻疹的传播，以模拟 COVID-19 的传播。该模型已经捕捉到爱尔兰人口的人口结构和通勤模式，因此，一旦适应了 COVID-19，它就可以提供关于大流行的重要见解，特别是在爱尔兰。我们首先研究需要调整的不同疾病参数来模拟 COVID-19 而非麻疹的传播，然后运行一组实验，最初将我们原始麻疹模型的模型输出与调整后的 COVID-19 模型的输出进行比较。然后我们报告了关于基本再生数的不同值如何的实验，${\mathrm{电阻}}_0$，影响模拟的爆发，并发现我们的模型表现如预期：越高${\mathrm{电阻}}_0$，感染的病原体越多。然后，我们展示了不同的干预策略（例如疫苗接种和学校停课）如何影响麻疹和 COVID-19 的传播，以及我们如何在我们的模型中模拟真实的大流行时间和干预措施。我们表明，在不同疾病组成部分之间具有相同的社会、环境和交通组成部分时，这两种疾病的结果非常不同，并且我们的 COVID-19 模型在爱尔兰莱特里姆县运行时预测的爆发长度与爱尔兰莱特里姆县的实际爆发，但与实际爆发相比，该模型导致受感染的病原体数量更多。这种病例的差异很可能是由于在模型中识别出所有 COVID-19 病例，而不是仅识别出那些经过测试的病例。一旦创建了基于代理的模型来模拟特定的复杂系统或社会，就可以调整疾病组件以模拟不同的传染病爆发。这使得基于代理的模型成为一种强大的工具，可用于帮助了解新出现的传染病的传播。