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.
Lohia, Nibhrat; Satluri, Rajesh; Moharana, Suchismita; and Kasarla, Venkat
"COVID-19 - A Graph Network Approach,"
SMU Data Science Review: Vol. 5:
3, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss3/1
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