SMU Data Science Review SMU Data Science Review
Using the Jaccard similarity coefficient, in the knowledge graph, this study is able to identify and explore relationships between COVID-19 cases as well as predict the vulnerability of general population in a vicinity.
Abstract
. The effects of COVID-19 and its spreads are attributed to various factors. This study uses CDC open-source data on COVID-19 effected population with features ranging from location to ethnicity, to create a Knowledge Graph to measure the similarity between COVID-19 cases and estimate the risk for people likely affected by COVID-19. This data could be used to find correlations between distinct factors, like ethnicity and pre-existing health conditions, to find the vulnerability of a given COVID-19 patient. Using the Jaccard similarity coefficient, in the knowledge graph, we are able to identify and explore relationships between COVID-19 cases as well as predict the vulnerability of general population in a vicinity.