In this paper, we study the Covid 19 disease profile in the Algerian territory since February 25, 2020 to February 13, 2021. The idea is to develop a decision support system allowing public health decision and policy-makers to have future statistics (the daily prediction of parameters) of the pandemic; and also encourage citizens for conducting health protocols. Many studies applied traditional epidemic models or machine learning models to forecast the evolution of coronavirus epidemic, but the use of such models alone to make the prediction will be less precise. For this purpose, we assume that the spread of the coronavirus is a moving target described by an epidemic model. On the basis of a SIRD model (Susceptible-Infection-Recovery- Death), we applied the EKF algorithm to predict daily all parameters. These predicted parameters will be much beneficial to hospital managers for updating the available means of hospitalization (beds, oxygen concentrator, etc.) in order to reduce the mortality rate and the infected. Simulations carried out reveal that the EKF seems to be more efficient according to the obtained results.

Predictive algorithms for the coronavirus epidemic are indispensable tools for monitoring the dynamic spread of COVID-19 and for implementing intervention and preparedness measures to mitigate the outbreak. Many of the existing mathematical models used for epidemic analysis are deterministic in nature, which may not fully capture the complex dynamics of disease transmission. In this paper, we introduce a novel stochastic predictive algorithm known as the LSM-EKF-SIRD-V algorithm. This algorithm combines a SIRD-V model, which accounts for susceptible, infected, recovered, deceased, and vaccinated cases, with the Least Square Method (LSM) and an Extended Kalman Filter (EKF). It provides daily dynamic predictions of the system’s parameters and is employed to analyze the COVID-19 disease profile in Algeria from January 29, 2021, to October 2, 2022. The primary goal of this approach is to create a decision-support system that empowers governments and health authorities with future pandemic statistics. This information enables them to adapt and optimize hospitalization resources, allowing for more effective intervention and preparedness measures to control the spread of the pandemic. Simulation results demonstrate the effectiveness of the proposed algorithm in accurately predicting the future dynamics of coronavirus spread based on historical and current case data.