A Dynamic Model for Predicting the Impact of Vaccine Avoidance and Health Protocols on the Covid-19 Outbreak

The COVID-19 pandemic is one of the most critical issues in the world today. Although many countries have been able to control the infection, much research is still needed to uncover the complex dynamics of virus transmission. This study aimed to utilize a mathematical model for analyzing epidemiological data of infectious diseases, aiming to comprehend their behavior, predict future trends, and investigate the influence of external factors on key indicators. This model extends the Susceptible-Exposed-Infected-Recovered (SEIR) framework by incorporating additional populations, such as vaccinated individuals, asymptomatic cases, and hospitalized patients. It also developed dynamics related to vaccination avoidance behavior and adherence to health protocols.  It is coded in MATLAB 2018-b software and is executed for 360 days. The results of the simulation showed that it is not possible to achieve the desired level of immunity from vaccine injection without following health protocols. On the other hand, considering the level of infection, increasing the participation rate in receiving vaccines and reducing the population of vaccines can control the epidemic. Therefore, a change in social behavior and an increase in the amount of vaccination can increase the awareness of society in reducing the avoidance of vaccination and improving compliance with health protocols.