具有时变动力学的房室传染病模型的全变差正则化
Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics
摘要：
分区流行病模型是流行病学中最流行的模型之一。对于参数（例如，传输率）表征这些模型，大多数研究人员将其简化为常数，而另一些人则设法检测其连续变化。在本文中，我们的目标是捕捉另一方面，不连续的变化，这更好地描述了许多值得注意的事件的影响，如城市封锁，野战医院的开放，以及病毒的突变，其影响应该是即时的。为了实现这一点，我们通过总变差来平衡模型的可能性，总变差调节模型参数的时间变化。为了推断这些参数，而不是使用蒙特卡罗方法，我们设计了一种新颖而简单的优化算法，称为迭代Nelder-米德，反复应用Nelder-米德算法。在模拟数据上进行的实验表明，我们的方法可以再现这些不连续性，并精确地描述流行病。
英文摘要：
Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events, such as city lockdowns, the opening of field hospitals, and the mutation of the virus, whose effect should be instant. To achieve this, we balance the model's likelihood by total variation, which regulates the temporal variations of the model parameters. To infer these parameters, instead of using Monte Carlo methods, we design a novel yet straightforward optimization algorithm, dubbed Iterated Nelder--Mead, which repeatedly applies the Nelder--Mead algorithm. Experiments conducted on the simulated data demonstrate that our approach can reproduce these discontinuities and precisely depict the epidemics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
