Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoods

使用泊松近似似然对流行病区室模型进行一致且快速的推断

• 批准号: 2266490
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2266490
• 关键词: Consistent; fast; inference; compartmental; models

The quantification and characterisation of infectious disease dynamics are essential for informing official decision makers in their response to emerging epidemics; they are also crucial in understanding previous outbreaks to better prepare for the future. The most popular paradigm for modelling the spread of a disease through a population is that of compartmental models. The likelihood for such models is inaccessible in all but the simplest cases, therefore, in order to perform inference, one needs to make approximations. Over the past few decades, computational advances have led to the development of many sophisticated and expensive simulation algorithms for inference on stochastic compartmental models. The aims of this project are to propose a class of computationally cheap and simple alternatives to these methods which are justified by rigorous consistency theory and demonstrated to be practically useful on real world data examples.

传染病动态的量化和特征对于向官方决策者提供信息以应对新出现的流行病至关重要；它们对于了解以前的疫情以更好地为未来做好准备也是至关重要的。对疾病在人群中的传播进行建模的最流行的范例是隔室模型。除了最简单的情况外，这种模型的可能性在所有情况下都是无法获得的，因此，为了进行推理，人们需要进行近似。在过去的几十年里，计算的进步导致了许多复杂而昂贵的模拟算法的发展，用于对随机间隔模型进行推断。这个项目的目的是提出一类计算上廉价和简单的替代方法，这些方法由严格的一致性理论证明是合理的，并在真实世界的数据例子中被证明是实用的。