Predicting COVID-19 Infection Groups using Social Networks and Machine Learning Algorithms

Today, social media has grown in usage to the point where it is often deeply intertwined with life offline. People share their thoughts, passions, and lives online, and in many ways, these social networks can be considered abstractions of real-world society. The idea for this research is that by modeling on these social networks, these glimpses into people's lives through their words and posts are capable of showing their current health situation, and their susceptibility to outside influences affecting it. The goal of this research project is to design and implement unsupervised machine learning techniques to group together sub-networks of connected individuals in hopes that it may be beneficial to current disease surveillance systems. Using Python programming language and the tools available to it, data was collected from the social network platform Twitter and analyzed using three clustering and centrality measurements. The criterion to be included in the data found tweets containing symptomatic keywords, like those of which experienced by people afflicted with the novel coronavirus disease (COVID-19). It is our findings in this research that by simulating the real-world connections that people have with their surrounding cliques using the ones that they exist within the virtual world, new possibilities for viral control and disease prevention become available using easily sourced, and quickly gatherable information.