Identifying Transmission and Risk Factors for COVID-19


  • Brittany Harbison
  • Eric Cabarlo
  • Dustin Lee
  • Logan Capes


Over the past few months, the rise of a novel coronavirus (COVID-19) has taken the world by storm. The virus has spread rapidly throughout many world communities, leading to its current classification as a pandemic. Many researchers have already published insights and observations on the disease to shed light on this new adversary, while additional research is still ongoing. The US government and medical professionals recently called on the data science community to reveal insights into this virus through the COVID-19 Open Research Dataset (CORD-19), a massive collection of coronavirus-related research. Contained within CORD-19 are insights into risk factors and transmission behavior for COVID-19.

This work assembles sections of CORD-19 data into an easily-readable format and uses this output to answer specific questions about the disease, such as environmental factors that correlate with increased transmission and other factors of risk that may contribute towards an individual patient contracting the virus. Additionally, the findings unveiled by this approach are further validated using machine learning algorithms on auxiliary datasets gathered from a few countries impacted by the virus.






Computer Science