COVID-19 Candidate Treatments, a Data Analytics Approach

COVID-19, short for “coronavirus disease 2019” has majorly affected millions of people worldwide. In the U.S. alone as of the end of this week (June 1, 2020), there have been 1,790,191 total cases, with 104,383 deaths. There have been 6,166,978 cases in the entire world, with 372,037 deaths, these are just the reported cases. Our focus in this research is in evaluating a repository of research papers to extract knowledge related to COVID-19 and possible treatments. Driven by the COVID-19 Open Research Dataset Challenge from Kaggle, we focused on a subset of that, COVID-19 Pulmonary Risks Literature Clustering. The second dataset we are using is from the Maryland Transportation Institute (MTI). The data is broken up into four categories: (1) Mobility and Social Distancing, (2) COVID and Health, (3) Economic Impact, and (4) Vulnerable Population. The data is extracted from NPR, ESRI, the COVID tracking project, CDC, and several other sources. MTI has been the source of several papers regarding mobility impact, social distancing, stay at-home orders, and non-pharmaceutical interventions.