As part of a project focused on visualizing uncertain data in a clear way, we chose COVID-19's spread as a perfect topic for exploring this concept. Using six observational studies on how quickly COVID-19 spreads among populations, we created a visualization which shows the estimations of infected populations using multipliers of positive tests found in the studies to provide a rough estimation of the degree of uncertainty about the size of the subclinical (asymptomatic) population of COVID-19 carriers.

This was a group project. Neil Oliver was primarily in charge of the code and implementation, Inhye Lee for the design, and Omer Leshem and I were responsible for exploring and interpreting the data, and writing up conclusions.

We used Vue.js in conjunction with a direct scrape of the NYC Open Health data repository, which has a csv file that is updated every 24 hours to accomplish a close to real-time data visualization tool.