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.
翻译:在本文中,我们建议对罗马尼亚Covid19的演变进行三个阶段的分析。在大流行病预测方面,有两个主要问题:第一个是报告的受感染和已恢复的人数不可靠,但死亡人数更准确。第二个问题是影响该流行病演变的因素很多。我们在本文件中建议分三个阶段进行分析。第一阶段以我们使用神经网络的典型SIR模型为基础。这提供了第一套每日参数。在第二阶段,我们建议改进SIR模型,将死者分为不同的类别。我们利用第一次估计和网格搜索,对参数进行每日估计。第三阶段用来确定参数的转折点概念(地方极端),我们将这些点之间的时间称为制度。我们根据SIRD的不同时间参数提出预测的一般方法。