A regime switching on Covid19 analysis and prediction in Romania

TL;DR

This paper presents a three-stage analysis of Covid-19 in Romania, combining neural network-enhanced SIR models and regime detection to improve understanding and prediction of pandemic dynamics.

Contribution

It introduces a novel three-stage framework integrating neural networks and regime switching for Covid-19 analysis in Romania.

Findings

Neural network-based SIR model provides initial parameter estimates.

Refined model separates deceased for better accuracy.

Regime detection identifies critical turning points for pandemic phases.

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.