Our project examines the CDC’s Coronavirus Data Tracker through the three key levels of a digital humanities project: sources, processing, and presentation. Each stage involved deliberate choices that shaped the analytical and narrative direction of our work, drawing on key ideas discussed throughout our course on digital methods in the humanities.
1. Sources
The dataset we selected is the CDC COVID-19 Data Tracker, which compiles data from state and local health departments across the United States. It includes information such as daily confirmed case counts, hospitalizations, deaths, test positivity rates, vaccination rates, and genomic surveillance of COVID-19 variants.
We chose this dataset because of its scale, public accessibility, and relevance to recent social and political events. It represents how institutions organize, prioritize, and structure data during a public health crisis. It also allowed us to think critically about how the boundaries of what gets recorded can reflect specific institutional goals or constraints.
We also considered the limitations in what the dataset excludes. For example, it lacks personal narratives, emotional experiences, and detailed information about vulnerable populations. These omissions reflect broader issues in how institutions define what counts as useful or valid information. This kind of analysis allowed us to treat the dataset not just as a scientific tool, but as a cultural artifact.
2. Processing
We used OpenRefine and Breve to clean and restructure the data before analysis. OpenRefine helped us fix inconsistencies, cluster similar values, and standardize formats across different datasets. Breve allowed us to filter large tables and simplify the data structure for easier analysis.
Key steps included:
- Cleaning location names and date formats
- Grouping data into seasonal or monthly intervals
- Filtering out incomplete or duplicate records
- Identifying gaps and reporting delays
These tools allowed us to clean and view the dataset easily, while also allowing us to determine which data grouping and sets were the best to use for data analysis.
3. Presentation
We used Tableau to create a series of interactive data visualizations for our project, WordPress, and TimelineJS:
- Pie Chart: Displays COVID-19 hospitalization counts by race and age group (0–17, ≥18, and all ages combined).
- Bubble Map: Shows the geographic distribution of COVID-19 deaths across the U.S. using scaled circles to represent volume at the county level.
- Bar Chart (Time Series): Depicts weekly COVID-19 deaths in the U.S. from March 2020 to July 2025.
- Grouped Bar Chart: Compares cumulative hospitalization rates by race and sex.
- Dot Plot: Tracks weekly COVID-19 vaccination counts for children and adults across months from 2024 to 2025.
- TimelineJS: Constructs a time line for the events following COVID-19 and after.
- WordPress: To construct the presentation of the project.
We paired each visual with explanatory text to guide interpretation. Our focus was not only on making the data accessible but also on encouraging critical reflection about how it was collected and what it might leave out. We utilized these tools since they were not only easy to maneuver but also helped to convey information to our viewers properly.
The Team
Gigi Davila : gigidavila29@g.ucla.edu
Gigi is a Statistics and Data Science major with a minor in Data Science Engineering. She contributed to data cleaning using OpenRefine and Breve, built several Tableau visualizations, contributed to the the annotated bibliography, and helped write the project’s data critique and technical sections.
Douglas Leung: djkleung185@g.ucla.edu
Douglas is a Statistics and Data Science major and a Cognitive Science major. He contributed to data cleaning using OpenRefine and Breve, built several Tableau visualizations, contributed to the annotated bibliography, and wrote much of the narrative of the project. Designed the project presentation website navigation and contributed to the data visualization.
Jingxian Shu: xianss03@g.ucla.edu
Jingxian is a Statistics and Data Science major. She developed the interactive timeline and conducted an analysis of U.S. COVID-19 death rate trends from 2020 to 2025 in relation to major public health and policy milestones. Her work aimed to highlight how governmental decisions and public health interventions shaped mortality outcomes over time. She also contributed to writing the data critique and the project narrative.
Acknowledgement:
Special Shout-Out to Dr. Jordan Galczynski for helping us put the team together and helping us with technical difficulties.
Special Shout-Out to Elle Themes for providing a template to construct this project.