In this paper, we propose an analysis of Covid19 evolution and prediction on Romania combined with the mathematical model of SIRD, an extension of the classical model SIR, which includes the deceased as a separate category. The reason is that, because we can not fully trust the reported numbers of infected or recovered people, we base our analysis on the more reliable number of deceased people. In addition, one of the parameters of our model includes the proportion of infected and tested versus infected. Since there are many factors which have an impact on the evolution of the pandemic, we decide to treat the estimation and the prediction based on the previous 7 days of data, particularly important here being the number of deceased. We perform the estimation and prediction using neural networks in two steps. Firstly, by simulating data with our model, we train several neural networks which learn the parameters of the model. Secondly, we use an ensemble of ten of these neural networks to forecast the parameters from the real data of Covid19 in Romania. Many of these results are backed up by a theorem which guarantees that we can recover the parameters from the reported data.
翻译:在本文中,我们提议分析Covid19对罗马尼亚的演进和预测,并结合SIRD的数学模型,这是古典模型SIR的延伸,把死者作为一个单独类别,因为我们不能完全相信报告的受感染或已康复的人数,所以我们的分析以更可靠的死亡人数为基础。此外,我们模型的一个参数包括受感染和受检测与受感染的比例。由于有许多因素对流行病的演进产生影响,我们决定根据过去7天的数据,特别是死者人数,来处理估计和预测。我们用神经网络进行估计和预测,分两步进行。首先,我们用模型模拟数据,我们训练几个神经网络,学习模型参数。第二,我们用10个这些神经网络的集合体来预测罗马尼亚Covid19的真实数据中的参数。许多这些结果都得到一个理论的支持,保证我们能够从所报告数据中恢复参数。