In this project, we analyzed the COVID-19 cases and deaths data in the state of Maryland, as it related to announcements made by Gov. Larry Hogan on mask mandates, indoor/outdoor dining, and travel and gathering restrictions.
We feel that the impact of this project was that it provided background to these public health and policy decisions, that for most of the reopening in 2020-2021, people felt was made with considerations for safety. Analyzing this data demonstrated that reopening was decided on in spite of, rather than because of, the state of COVID at the time. Now, as we go into 2022 amid a huge surge in COVID cases from the Omicron variant, this project helps add some context to compare guidance to outbreak numbers at the time.
All of the data we collected was from The New York Times’ Covid-19 national data on GitHub. After downloading it, we isolated the Maryland data and focused solely on that section. To construct the timeline of mandates and announcements, we worked backwards and looked at news from various outlets, often The Baltimore Sun, so we could organize which announcements happened when. The website page was built using HTML and CSS, and the charts were made with Flourish. Before coding the final product, we mocked up the design and tested out multiple different options using pen and paper sketches, as well as Adobe XD wireframing and Google Slides.
What was the hardest part of this project?
The hardest part of this project was matching the data between the time of restrictions and number of cases in a given timeframe. And finding an appropriate way to visualize the data. We chose to put the 7-day average and the daily cases on the same charts, even though this decision required slightly different y-axes. The small difference in axes didn’t impact the visualization of the data very much, and in our opinion, didn’t mislead from our point. We also chose to omit hospitalizations, but kept deaths in the chart despite it being small. Since our main focus was to contextualize the public policy decision making with the impact it had on cases, we focused most on daily cases and the 7-day average. The average was often referenced as justification for the public health and policy decision making, so it was important to bring that data to the forefront.
What can others learn from this project?
Other journalists can learn to question the effectiveness of public policy and restrictions and to approach the COVID pandemic from a data-based perspective. With something that can change so rapidly, like COVID cases and 7-day averages, even when a decision may be justified by a dip in the data, it can immediately be unjustified as cases rise, as a result of a new guidance. All COVID-related decision making seemed to be made on various different justifications, like how long the average was below X number, and when that was reported on without visualization, people may not really realize where we are at. These two sentences really demonstrate how we used the data to show what went into the reopening decisions: “On May 8, four days after Maryland broke an average of 1,000 cases per day, Gov. Hogan was ready to begin Phase 1 of Maryland’s reopening plan. There was an average of 958 cases per day between May 8 and June 4, 2020, an increase of 409% from the seven-day average on March 30.” When we realized this, we knew the project had achieved our goal of pointing out that, despite reopening, things weren’t getting safer for the public.