In the article, the authors address visualizations of COVID cases, including related satellite mages of air pollution in Southern California and China (generated by NASA/ESA) as well as of mass graves in Iran.
First, they provide basic framing of how to critically read air pollution satellite imagery. Connections between COVID-19 measures and improvements in air pollution are not identifiable in a straightforward way.
"Figure 1a, for instance, uses bright magenta to indicate greater concentrations of nitrogen dioxide and light blue to signify cleaner air. However, such color choices can be misleading: there is no material correlation between nitrogen dioxide and the color magenta; and reduced traces of this chemical do not turn the sky a paler shade of blue. [...] color-coding selections imply, satellite images are not just scientific; they are cultural as well."
Second, they point out the paradox role of satellite imagery to account for the inequitable impact of COVID-19
"satellite image, from a US satellite operator, locates pandemic “excesses” in an Iranian “elsewhere.” But this is an increasingly deceptive proposition, given that the United States has one of the highest COVID-19 per capita transmission and fatality rates in the world."
Third, they draw comparisons between the "hockey stick" visualization of global Climate Change and the various "curves" used to display COVID-19 developments:
From a disaster media perspective, the film’s global warming graph depicts a dramatic climate shift, projects imminent catastrophe, and issues a world warning. Its circulation in global media culture for the past fifteen years potentially informs the ways people are engaging now with similar-looking charts of coronavirus death and illness. Historically, news media have relied on sensationalistic photos of human suffering to convey a sense of disaster, but in the age of big data and the current pandemic, numbers speak, and graphs and curves tend to dominate the mediascape. In both cases, scientific experts and publics must grapple with how these graphs make meaning, what datasets they rely upon, and how these media come to stand in for highly complex conditions.
Finally, they remark that COVID-19 visualizations are always incomplete - because of lack of testing and withholding of data - but also because stories of e.g. workers are missing. They reference the cover of the New York Times (May 24, 2020) that displayed the names of 100,000 people who had died from COVID.