Mathematical Modeling for Virus Immunization and Vaccination
Abstract
A global pandemic, COVID-19, broke out in 2020, causing an emergency. The whole world was in deep trouble, which forced the entire world to go into lockdown, prompting a worldwide effort to develop vaccines. By then, several countries, including the USA, UK, Russia, India, China, and Israel, started producing a range of vaccines to combat the virus. The vaccination drive started vaccinating vulnerable populations, including older adults and individuals with underlying health conditions. The efficacy and effectiveness of these developed vaccines vary from vaccine to vaccine, and there is a need to calculate the efficacy of each developed vaccine. The effectiveness of these vaccination campaigns can be modeled mathematically using differential equations to simulate population dynamics, helping to predict when herd immunity might be achieved. The vaccination drive helped in controlling this pandemic, and a mathematical model helped us identify potential threats emerging from new variants of the coronavirus. Getting full immunity in the population and herd immunity is very difficult, but efforts are made to predict. COVID-19 deaths can be mitigated if a vaccination scenario is modeled using the SVIR (susceptible, vaccinated, infected, and recovered). Simulation results show that vaccination and timing have an impact on the total population for achieving herd immunity.