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Verification of an agent-based disease model of human Mycobacterium tuberculosis infection
International Journal for Numerical Methods in Biomedical Engineering ( IF 2.1 ) Pub Date : 2021-04-25 , DOI: 10.1002/cnm.3470
Cristina Curreli 1, 2 , Francesco Pappalardo 3 , Giulia Russo 3, 4 , Marzio Pennisi 5 , Dimitrios Kiagias 6 , Miguel Juarez 6 , Marco Viceconti 1, 2
Affiliation  

Agent-based models (ABMs) are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for ABMs that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs.

中文翻译:

人类结核分枝杆菌感染的基于代理的疾病模型的验证

基于代理的模型 (ABM) 是一类强大的计算模型,广泛用于模拟许多不同应用领域中的复杂现象。然而,最关键的方面之一,在文献中研究不足,认为模型可信度评估的一个重要步骤:解决方案验证。本研究通过提出 ABM 的通用验证框架来克服这一限制,该框架旨在评估与模型相关的数值误差。详细描述了由两个主要验证研究(确定性和随机模型验证)组成的分步程序,并将其应用于特定的关键任务场景:UISS-TB 数值近似误差的量化,一种 ABM为预测肺结核的进展而开发的人体免疫系统。
更新日期:2021-04-25
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