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罗马尼亚新冠疫情的制度转换分析。

A regime switch analysis on Covid-19 in Romania.

机构信息

Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania.

Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Bucharest, Romania.

出版信息

Sci Rep. 2022 Sep 13;12(1):15378. doi: 10.1038/s41598-022-18837-x.

Abstract

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.

摘要

本文提出了罗马尼亚新冠疫情发展的三阶段分析。在大流行预测方面存在两个主要问题。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的演变。在本文中,我们提出了三阶段分析。第一阶段基于经典的 SIR 模型,我们使用神经网络进行分析。这提供了一组初始的每日参数。在第二阶段,我们提出了对 SIR 模型的改进,其中我们将死者分为一个单独的类别。通过使用第一个估计值和网格搜索,我们给出了每日参数估计值。第三阶段用于定义参数的转折点(局部极值)的概念。我们将一个时期称为参数的一个时期。我们概述了一种基于 SIRD 时变参数的一般预测方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f945/9470547/4aa9758ac6d6/41598_2022_18837_Fig1_HTML.jpg

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