On forecasting of COVID-19 transmission in Saudi Arabia and Egypt using reservoir computing model

This work is devoted to introduce a reliable, fast and highly accurate reservoir computer machine learning scheme to forecast time evolution of COVID-19 pandemic. In particular, the COVID-19 official data related to susceptible cases, confirmed cases, and recovered cases in Egypt and Saudi Arabia are collected. They employed as the training data for suggested reservoir computer (RC) model. Then, detailed simulation experiments are carried out within specified time periods. The evolution of COVID-19 in Egypt and Saudi Arabia are predicted on the subsequent times intervals and compared with real validation test data. The forecasting accuracy is improved by computing the optimal output matrix which minimizes the normalized root mean square errors (NRMSEs). The performance of RC scheme is evaluated when different-size training data, different-size test data, and different number of internal nodes are used. The comparisons with the robust LSTM deep learning techniques are performed. It is shown that the presented RC-based forecasting technique is more accurate for long-time forecasting, faster, and has lower computational cost. © 2023 THE AUTHORS

A discrete-time epidemic model for the analysis of transmission of COVID19 based upon data of epidemiological parameters.

The forecasting of the nature and dynamics of emerging coronavirus (COVID-19) pandemic has gained a great concern for health care organizations and governments. The efforts aim to to suppress the rapid and global spread of its tentacles and also control the infection with the limited available resources. The aim of this work is to employ real data set to propose and analyze a compartmental discrete time COVID-19 pandemic model with non-linear incidence and hence predict and control its outbreak through dynamical research. The Basic Reproduction Number ( R 0 ) is calculated analytically to study the disease-free steady state ( R 0 < 1 ), and also the permanency case ( R 0 > 1 ) of the disease. Numerical results show that the transmission rates α > 0 and ß > 0 are quite effective in reducing the COVID-19 infections in India or any country. The fitting and predictive capability of the proposed discrete-time system are presented for relishing the effect of disease through stability analysis using real data sets.

Preventive control strategy on second wave of Covid-19 pandemic model incorporating lock-down effect

This study presents an optimal control strategy through a mathematical model of the Covid-19 outbreak without lock-down. The pandemic model analyses the lock-down effect without control strategy based on the current scenario of second wave data to control the rapid spread of the virus. The pandemic model has been discussed with respect to the basic reproduction number and stability analysis of disease-free and endemic equilibrium. A new optimal control problem with treatment is framed to minimize the vulnerable situation of the second wave. This system is applied to study the effects of vaccines and treatment controls. Numerical solutions and the graphical pre-sentation of the results predict the fate of India's second wave situation on account of the control strategy. Lastly, a comparative study with control and without control has been analysed for the exposed phase, infective phase, and recovery phase to understand the effectiveness of the controls. This model is used to estimate the total number of infected and active cases, deaths, and recoveries in order to control the disease using this system and studying the effects of vaccines and treatment controls. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).