A regime switching on Covid19 analysis and prediction in Romania
This work addresses pandemic prediction challenges for public health in Romania, but it appears incremental as it builds on existing SIR models with minor modifications.
The authors tackled the problem of unreliable infection and recovery data in Covid-19 prediction by proposing a three-stage analysis using a modified SIR model with neural networks and regime switching, applied to Romania, but no concrete numerical results or performance metrics are provided.
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.