COVID-19 pandemic triggered a global crisis, whether it comes to a huge global health emergency or to the global economic crisis situation. It is one of the greatest challenges this generation is facing. Computational simulations are playing a huge rule in the prediction of the current pandemic. Such simulations enable early predictions for future projections of the pandemic and are useful to estimate the efficiency of control action taken against this virus. The SEIR (Susceptible-Exposed-Infectious-Recovered) model is a commonly used model to compute the simulations of any infectious viral diseases and was widely used before to model and simulate SARS, EBOLA, Spanish Flu, etc. This paper presents a modified SEIR model with additional parameters taken into consideration such as the death, recovered and recovered with the chance of being infected again, vaccination and control efficiency;where the control represents the effectiveness of the lockdown. This factor is being controlled in order to extend the projections into controlled death, recovery, and infection. Specific information including time delay on the development of the pandemic due to control action measures, ageing factor of the population, and resusceptibility with temporal immune response are also included in the model. After that, the model examines the outcome of the system after adding a controllable vaccine with taking into consideration the vaccination rate and vaccine's efficacy. The numerical results are demonstrated to show the predictability range of this model.
Development of a Robust Mathematical Model to Estimate COVID-19 Cases in Lebanon Based on SEIRDV Modified Model