论文标题
与时变动力学的隔室流行模型的总变异正则化
Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics
论文作者
论文摘要
隔室流行模型是流行病学中最受欢迎的流行模型之一。对于表征这些模型的参数（例如，传输速率），大多数研究人员将它们简化为常数，而其他一些研究人员则设法检测其连续变化。在本文中，我们旨在捕获不连续的变化，更好地描述了许多值得注意的事件的影响，例如城市锁定，开放野外医院和病毒的突变，其作用应该是即时的。为了实现这一目标，我们通过总变化来平衡模型的可能性，从而调节模型参数的时间变化。为了推断这些参数，我们不是使用蒙特卡洛方法，而是设计了一种新颖而又直接的优化算法，称为迭代的nelder-铅，该算法反复应用了Nelder--Mead-Mead算法。对模拟数据进行的实验表明，我们的方法可以再现这些不连续性，并精确地描绘了流行病。
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.
