用变形检验、模型检验和可视化分析验证区隔流行病学模型

A. Ramanathan, C. Steed, L. Pullum
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引用次数: 25

摘要

流行病学中的区隔模型被广泛用于模拟疾病传播机制,并了解在发生大范围流行病爆发时如何最好地控制疾病。然而,社区内的一个重大挑战是开发可用于严格验证和验证这些模型的方法。在本文中,我们提出了一种量化和验证区隔流行病学模型在几种常见建模情景下的行为特性的方法,包括:出生率/死亡率和多宿主/病原体物种。我们建立了一个工作流程,使用变形测试,新颖的可视化工具和模型检查来深入了解分区流行病学模型的功能。我们的初步结果表明,变质试验可以用来验证这些模型的实现,并提供对这些数学模型可能失败的特殊条件的见解。可视化前端允许最终用户扫描这些模型中常用的各种参数,以阐明可能发生流行病的条件。此外,使用流程代数指定这些模型允许自动构建可以使用模型检查严格验证的行为属性。总之,我们的方法允许检测实施错误以及处理在这种情况下,分区流行病学模型可能无法提供对疾病传播动态的见解。
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Verification of Compartmental Epidemiological Models Using Metamorphic Testing, Model Checking and Visual Analytics
Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a significant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to quantify and verify the behavioral properties of compartmental epidemiological models under several common modeling scenarios including: birth/death rates and multi-host/pathogen species. We build a workflow that uses metamorphic testing, novel visualization tools and model checking to gain insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Furthermore, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics.
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