Covid Vac Net maps the vaccination candidate connectome on an interactive digital world map using R & Java Script as well as Gephi. We pulled data from verified sources, such as the CDC and New York Times, and through exploratory data analysis, we shaped our data to fit our models. Then, we developed an algorithm using R to calculate vaccination percentages for individual countries as well as the significance of a stringency index on a country’s ability to institute regulations. After the numbers were crunched, we used machine learning to predict the risk of infection among vaccinated candidates on a global scale. With a neural network through an 80-20 split of data, we received accuracies in the range of 93% to 99%, depending on the vaccine candidate.
University of California Irvine