The COVID-19 pandemic is one of the first global crises universally communicated through data. We read dashboards, signed up for alerts, and scrolled through countless pieces of data journalism summarizing new studies in an effort to be more informed in the middle of a crisis.
Plotting numbers on a chart implies a certain factfulness and objectivity in the data, but what we saw in the pandemic was just how uncertain and incomplete those data could be. Many of the challenges we faced as data designers in a pandemic echo the broader challenges of visualizing and communicating health information.
Issues of interoperability or lags in reporting systems can leave us with incomplete information. Changes in any single measure can be shaped by many others, like the impact of testing on case counts). The wide variance in the adoption of robust, modern digital health architecture can shape the timeliness and completeness of the data and our ability to make cross-country comparisons. Aggregate statistics formed from these incomplete data influence individual decision making and policy setting.
Perhaps most importantly: health information is intensely personal. Unlike statistics about budgets or infrastructure, records in health data typically represent people with individual stories that can shape the ethical weight of our design decisions. During this talk, explore the challenges we face visualizing and communicating often incomplete data in the midst of a global pandemic, and how that learning applies more broadly across all visualization of health information.