A Global Network Analysis of COVID-19 Vaccine Distribution to Predict Breakthrough Cases among the Vaccinated Population

Abstract

As the COVID-19 pandemic began spreading worldwide in late 2019 and early 2020, many vaccine candidates were developed to combat the disease. However, new COVID-19 variants such as Omicron and Delta continue to emerge globally despite advancements in vaccine technology, leaving certain countries and variants more vulnerable than others to future outbreaks of these variants. This research aims to analyze the susceptibility of different countries to a COVID-19 outbreak, present the first visualization of the spread of COVID-19, and predict which countries are at greater risk for future outbreaks of new variants based on various factors. We created interactive maps to understand the pandemic’s spread and identify high-risk countries based on their vaccination percentages. Then we employed binary classification, K-nearest neighbors (KNN), and neural network machine learning models to predict each country’s risk factor. The risk factor determines whether a country is safe from a new COVID-19 variant based on vaccine percentage and government stringency. The neural network achieved the highest accuracy, classifying countries as high risk or low risk with 94% accuracy. Inspired by the Albert Barabasi model, we graphed connections between countries based on vaccination percentages. These graphs illustrate the correlation between the two countries and better demonstrate how their vaccination rates relate to the probability of a new COVID-19 outbreak.

Keywords

• neural network
• data science
• network science
• COVID-19 vaccines
• breakthroughs
• global connectome