Data journalism played a major role during the first wave of the global coronavirus pandemic, gaining in significance and status with its promise of evidence-driven reporting on the spread of COVID-19. Figures and charts showing daily infection rates, hospitalizations, and deaths became commonplace in reporting on COVID-19, and in discussions among citizens at home or on social media.

The numbers from public authorities and data journalism teams presented a stark picture of the devastating impact of the virus across the world. But they also risked leaving audiences with a misleading picture of how their city, region, or country was faring in the struggle against the virus.

The impact and role of data journalism must be understood in relation to the formative and largely unknown characteristics of the first wave itself. Many citizens turned into news omnivores with regards to the coronavirus, keen to learn about its spread and how to adapt to the new reality. Several months later, this tremendous interest seemed to cool down.

During the second half of 2020, many have faced a second, seemingly more devastating wave of the virus. As this second wave takes us into 2021, data journalists will have to work harder to explain the latest COVID-19 figures related in light of more widespread knowledge about the virus — including being open about the uncertainties in their reporting.

Counting cases

The epistemology of data journalism has long held the promise of being a more accurate and reliable form of reporting, using social science methods to analyze quantitative data using computers. Doing accurate and reliable data journalism on COVID-19 is challenging, and during 2021 data journalists will have to advance their practices to maintain perceptions of being credible, relevant, and worthwhile to the public.

With more citizens discussing, analyzing, and questioning health data than ever before, the expectations from journalism are higher. The challenges of reporting COVID-19 underscore key priorities for data journalism going forward — the need for caution when dealing with health data, for expertise in understanding it, and for transparency in acknowledging the limitations of it.

The pandemic has brought to the fore many of the reservations of data journalism. Perhaps the most prominent has been the need for, and access to, reliable, representative, and relevant data. Journalists have depended on data from national governments, scientific bodies, international organizations, universities, and more to report on the spread of the virus, the human cost, and the strain on hospitals.

The problem is that, as Nate Silver argued early in the pandemic, “coronavirus case counts are meaningless,” especially when it comes to international comparisons. Aside from inconsistencies in how cases are counted between countries, the data also depends heavily on the extent of testing in a given location.

Reporting accurately on the number of people who have died due to COVID-19 depends on how these deaths are counted. In some cases, the deaths are only counted if there has been a confirmed test of the virus. A further complication is whether COVID-19 is reported as the cause of death or as a contributory factor.

COVID-19 caveats and anomalies

At a time when the public is seeking clarity and certainty, reporting with all sorts of caveats about the nature of the data on COVID-19 doesn’t make for good journalism.

During the first wave, and on a daily basis, case counts and deaths dominated the headlines, presented by journalists as definitive and authoritative statements. To their credit, some news organizations, such as the BBC, Financial Times, and The New York Times, have been reporting excess mortality rates, which is widely acknowledged by health experts as a more reliable approach.

COVID-19 has surfaced the challenges of data journalism, above all its dependence on institutional data and a lack of time, resources, expertise, or ability to scrutinize the underlying assumptions in a data set.

It also presents an opportunity to do better in 2021. It’s an opportunity to be more critical about institutional data, more cautious in its reporting, and more transparent about the uncertainties of what we know. If journalists don’t identify and explain strange anomalies in the data, they’ll hear about it from the public.