罗马尼亚Covid19的一个政权转换分析和预测
A regime switching on Covid19 analysis and prediction in Romania
摘要：
在本文中，我们提出了Covid 19在罗马尼亚演变的三个阶段分析。在大流行预测方面有两个主要问题。第一个问题是，报告的感染和康复人数不可靠，但死亡人数更准确。第二个问题是，有许多因素影响了大流行病的演变。在本文中，我们提出了三个阶段的分析。第一阶段是基于经典的SIR模型，我们使用神经网络。这提供了第一组每日参数。在第二阶段，我们提出了一个改进的SIR模型中，我们将死者分成一个不同的类别。通过使用第一估计和网格搜索，我们给出了参数的每日估计。第三阶段用于定义参数的转折点（局部极值）的概念。我们把这两点之间的时间称为状态。我们概述了一个一般的方法，根据时变参数的SIRD作出预测。
英文摘要：
In this paper we propose a three stages analysis of the evolution of Covid19 in Romania. There are two main issues when it comes to pandemic prediction. The first one is the fact that the numbers reported of infected and recovered are unreliable, however the number of deaths is more accurate. The second issue is that there were many factors which affected the evolution of the pandemic. In this paper we propose an analysis in three stages. The first stage is based on the classical SIR model which we do using a neural network. This provides a first set of daily parameters. In the second stage we propose a refinement of the SIR model in which we separate the deceased into a distinct category. By using the first estimate and a grid search, we give a daily estimation of the parameters. The third stage is used to define a notion of turning points (local extremes) for the parameters. We call a regime the time between these points. We outline a general way based on time varying parameters of SIRD to make predictions.
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。
