Forecasting of COVID-19 in Egypt and Oman using Modified SEIR and Logistic Growth Models

Abstract

Understanding the transmission dynamics of the novel coronavirus is the concern that attracted many researchers nowadays. In this paper, two mathematical models, modified SEIR and logistic growth, were implemented in Matlab to predict the transmission of COYID-19 in Egypt and Oman. To estimate the models’ parameters, the reported data were used to fit the models using Nelder-Mead, Levenberg-Marquardt, and Trust-Region-Reflective optimization algorithms. Then, a sensitivity analysis was made to understand the effect of different parameters on the models’ prediction. The application of the two models on the reported data was compared despite their different nature. It was shown and verified that the two models are highly dependent on the parameters’ values, referring to the importance of determining their estimates using an optimization algorithm. It was found out that the most dominant parameter is the one denoting the rate by which susceptible people are protected, which emphasizes the effect of social distancing and quarantine.