A Global Network Analysis of COVID-19 Vaccine Distribution to Predict Breakthrough Cases Among the Vaccinated Population
Authors: Padmanabhan Sathya , Bhattacharya Medha , Bodapati Pragyaa , Zhang Eddie , Das Anaisha , Jahanikia Sahar 
Published: November 2023

Abstract
As the COVID pandemic began to spread among the whole world in late 2019 and early 2020, it changed everyone's life in many ways. There were numerous hospitalizations and deaths in countries as well as thousands of people stuck in quarantine all over the world as the disease quickly spread across the world. In response, several pharmaceutical companies and vaccine manufacturers, such as Pfizer and Moderna, promptly released vaccines around the world to mitigate the spread of COVID-19. However, in spite of these advancements, new COVID-19 variants such as Omicron and Delta continue to emerge in all areas of the world, leaving some countries and variants more vulnerable than others to a future outbreak of that variant. Even though some may be more effective against certain variants than others, no vaccine has been fully effective, resulting in breakthrough cases in which a vaccinated individual may be infected with COVID-19. This project aims to analyze the susceptibility of different countries to a COVID 19 outbreak. Furthermore, we aim to predict which countries are at a higher risk for a major COVID-19 outbreak with a future variant based on various factors such as their vaccination rates for each vaccine candidate and the strictness of government regulations.
Interactive global maps can be used to visualize COVID-19 data and make it easier to interpret and analyze. The visualizations are also useful in making sense of the results of our machine learning models. We used a range of colors to demonstrate how different countries are at higher or lower risk for a future COVID-19 outbreak. We created the interactive maps using R and Javascript. Through these global maps, viewers can understand the spread of pandemic and which countries are high-risk, allowing them to take appropriate action based on the data. We collected data from The New York Times, CDC, and Johns Hopkins Coronavirus Resource Center to calculate vaccination percentages for each nation. Using R packages and Javascript, we developed digital maps that showed these vaccination percentages. Additionally, we utilized Binary Classification, K-Nearest-Neighbors (KNN), and neural networks machine learning models to predict the risk factor of each country. The risk factor determines whether a country is safe or not to a new COVID-19 variant based on vaccine percentage and government stringency. Vaccine percentage is the percent of the country’s population who has been vaccinated with a certain vaccine candidate. Government stringency is the strictness of government regulations to COVID-19 in a country, and the value is determined by different factors such as quarantining and social distancing mandates. We developed a neural network model using R that could classify countries as high-risk or low-risk with an accuracy rate of approximately 94% for all vaccine data. Inspired by the Albert-Barabasi model, we graphed the connections between counties based on vaccination rates using Gephi. Gephi is a tool that allows for an open source network analysis and visualization software package. The networks can help pinpoint nations that are more likely to be greatly affected by a certain COVID-19 variant. As an example, if two countries share a strong connection, as seen with the thickness of nodes or the color and thickness of edges, for a particular vaccine distributor, they are more likely to both be affected by a new variant which may be immune to that specific vaccine candidate. At this point, the vaccine is not able to fight against the variant making the vaccine ineffective as well as endangering the countries that use it the most. Susceptible countries are countries that have most of their population vaccinated around that one vaccine because then if there is an issue with that one vaccine then the majority of their population will struggle.