论文标题
数据驱动的控制理论范式用于大流行，并应用于COVID-19
A Data-Driven Control-Theoretic Paradigm for Pandemic Mitigation with Application to Covid-19
论文作者
论文摘要
在本文中，我们引入了一种新的控制理论范式来减轻病毒的传播。为此，我们的离散时间控制器旨在减少日常死亡的数量，从而减少死亡人数的累积数量。与许多现有文献相反，我们不依赖于潜在的复杂病毒传播模型，该模型必须根据手头大流行的“细节”进行定制。对于诸如Covid-19之类的新病毒，驱动建模过程的流行病学可能尚不清楚，并且数据有限的模型估计可能不可靠。考虑到这一动机，此处描述的新范式是数据驱动的，并且在很大程度上，我们避免了通过仅关注大流浪汉通常的两个关键数量来建模困难：加倍时间，用$ d（k）$表示，$ d（k）表示$θ（k）$。迄今为止，我们的数值研究表明，我们有吸引力的简单模型可以为真实数据提供合理的拟合。鉴于时间是在持续的全球健康危机期间的本质，因此本文的目的是引入这种新的范式来控制从业者并描述我们当前结果提出的许多新的研究指示。
In this paper, we introduce a new control-theoretic paradigm for mitigating the spread of a virus. To this end, our discrete-time controller, aims to reduce the number of new daily deaths, and consequently, the cumulative number of deaths. In contrast to much of the existing literature, we do not rely on a potentially complex virus transmission model whose equations must be customized to the "particulars" of the pandemic at hand. For new viruses such as Covid-19, the epidemiology driving the modelling process may not be well known and model estimation with limited data may be unreliable. With this motivation in mind, the new paradigm described here is data-driven and, to a large extent, we avoid modelling difficulties by concentrating on just two key quantities which are common to pandemics: the doubling time, denoted by $d(k)$ and the peak day denoted by $θ(k)$. Our numerical studies to date suggest that our appealingly simple model can provide a reasonable fit to real data. Given that time is of the essence during the ongoing global health crisis, the intent of this paper is to introduce this new paradigm to control practitioners and describe a number of new research directions suggested by our current results.
