论文标题
政权开启了罗马尼亚的COVID19分析和预测
A regime switching on Covid19 analysis and prediction in Romania
论文作者
论文摘要
在本文中，我们建议对罗马尼亚的Covid19的演变进行三个阶段分析。 关于大流行预测，有两个主要问题。第一个是事实，即受感染和恢复的数量是不可靠的，但是死亡人数更准确。第二个问题是有许多因素影响了大流行的演变。 在本文中，我们提出了三个阶段的分析。第一阶段是基于我们使用神经网络进行的经典SIR模型。这提供了第一组每日参数。 在第二阶段，我们提出了SIR模型的改进，其中将死者分为不同的类别。通过使用第一个估计和网格搜索，我们每天对参数进行估计。 第三阶段用于定义参数的转折点（本地极端）的概念。我们将这些要点之间的时间称为政权。 我们概述了基于时间变化的SIRD参数以做出预测的一般方式。
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.
