A regime switch analysis on Covid-19 in Romania

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.

Automated summary

In this paper, a three-stage analysis of the evolution of Covid19 in Romania is proposed. Two main issues in pandemic prediction are discussed and simulated, including the unreliability of reported numbers of infected and recovered cases, and the impact of various factors on the pandemic's evolution. The authors propose a simulation method based on the classical SIR model and neural network, refine the model by separating deceased individuals into a distinct category, estimate daily parameters through a grid search, and define turning points for parameter changes. A general approach using time-varying parameters of SIRD is outlined for making predictions.