A deep dive into a database of the race and ethnicity of COVID-19 cases and deaths by state and how to use it to report on effective responses in communities hardest hit by the fallout from coronavirus
Communities hardest hit by the pandemic are also the ones most likely to suffer deeply in the aftermath. At the same time, effective responses to some of the problems confronting these communities are beginning to emerge. But those stories are not always as widely known as the “problem” stories, in some instances because they’re harder to find. One way to find them is by using data analysis to identify positive outliers, examples of responses that are working better than the rest. In this session, we’ll share examples of data-informed stories that dig into these responses, as well as explore how to use, in your coverage, a SJN-maintained database of the breakdown by race and ethnicity of COVID-19 cases and deaths from more than 120 state and local governments. We’ll walk through how to identify positive outliers within the database in your reporting process and show you how to adapt the reporting to your community.
Suggested Speaker(s)
- Ingrid Sturgis
Assistant Professor, New Media, Howard University - Matthew Kauffman
Investigative reporter and data journalist, Solutions Journalism Network