Computational Model of Vaccination Effects for COVID-19

Abstract

Modeling is essential for understanding the spread of infectious diseases. We applied computational modeling to investigate the infections of the ongoing pandemic caused by the SARS-CoV-2 virus (COVID-19) using Processing visualization software. This computational model provides visual and stochastic data of spread and recovery of general viral infections. An open source code for the spread of viral infection was modified. Variables such as death rate and infection rate were inputted according to available data on COVID-19, making this model COVID-19 specific. We separated the population into high, medium, and low risk groups by age. We ran simulations with a varying percentage of population who were vaccinated and recorded the statistics of the population who recovered, escaped infection, and died. Higher percentage of vaccination in the population resulted in lower mortality, higher percentage of people escaping infection, and a flatter infection curve. The mortality rates were as follows: 16.8, 13.4, 7.5, and 2.6 per thousand for 0%, 25%, 50%, and 75% vaccination, respectively. The significance of this model is that it shows that a lower mortality rate is directly correlated with a higher vaccination rate. Future directions of this research would include varying the vaccination efficacy and altering baseline parameters to mimic behavior of COVID-19 hotspots. This model can be used as a tool to simulate possible vaccination scenarios to better inform strategies of future COVID-19 vaccinations.