UTLDR：传染病和公共干预建模的基于主体的框架
UTLDR: an agent-based framework for modeling infectious diseases and public interventions
摘要：
如今，由于SARS-CoV-2大流行，流行病建模正经历着来自不同研究领域的研究人员不断增长的兴趣。事实上，关于计算流行病学的大量文献为分析研究和旨在预测性和规定性情景描述的新模型的定义提供了坚实的基础。为了方便使用扩散模型，在过去十年中提出了几个编程库和工具：然而，据我们所知，没有一个是明确设计的，允许其用户将公共干预纳入其模型。在这项工作中，我们介绍了UTLDR，一个框架，可以模拟几个公共干预措施（及其组合）对疫情过程的展开的影响。UTLDR支持分区模型的增量设计，并在复杂的交互网络拓扑上模拟它们。此外，它允许整合关于分析群体的外部信息（例如，年龄、性别、地理分配和流动模式（\dots），并使用它来分层和细化设计的模型。在介绍该框架之后，我们提供了一些案例研究来强调其灵活性和表达能力。
英文摘要：
Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns\dots) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.
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