Might, Maybe, Must: The Evolution of Certainty in COVID-19 Reporting
It’s tempting to believe that facts speak for themselves. That, no matter how it is presented, data is data.
Science communication reveals that is not the case. How facts are communicated influence their reception, their critique, and the (in)action they spur. COVID-19 reporting is a clear example of how rhetoric can deeply change a reader’s perception of scientific fact. Compare, for example, two pieces by former Washington Post editor Andrew Freedman, published a year apart.
As the Northern hemisphere began to thaw in March of 2020, questions circulated about how the pandemic would evolve over the warm summer months. The Post took a look at a preprint uploaded to the research site SSRN that suggested rates of infection from COVID-19 may drop over the summer. The preprint’s authors, both affiliated with the Massachusetts Institute of Technology, postulated that the virus may spread more slowly in areas warmer than 63º F with high humidity. In the same article, Freedman and his colleague Simon Denyer also spoke to researchers who cautioned that summer would offer little respite, shrouding the preprint’s analysis in a veil of skepticism.
By March of 2021, scientific consensus had changed about the virus’ seasonality. Freedman reported on initial findings from a World Meteorological Organization panel that warm weather alone won’t decrease COVID-19 rates. The panel of 16 interdisciplinary experts from five continents analyzed peer-reviewed research papers published until January of 2021, just as new variants began to emerge. They found that human behavior –such as mask-wearing and social distancing– impacts virus transmission more than environmental factors do.
Is the change in tone due solely to the growing body of scientific research, or could there be other factors at play? In the year between the two articles, many aspects of science reporting didn’t change. Freedman turns to scientific researchers to interpret the findings of studies rather than conducting his own analyses. He cites individuals from five institutions of higher education in the first piece and sixteen in the second, nodding to the rigorous body of COVID-19 research that they had conducted. Names like the MIT and Johns Hopkins University, in addition to titles like immunologist, virologist, and earth scientist, encourage the reader to have faith in the article’s assertions by implicitly drawing upon the centuries of prestige held by these institutions.
Both pieces also express doubt in their findings, noting in 2020 that, “research is only just getting underway” and in 2021 that “no firm conclusions [about environmental effects on virus survival] can be drawn for COVID-19 at this time.” Science is an iterative process, so it follows that even years into a pandemic, questions remain.
But a deeper analysis of the news stories reveals key differences in the tools used to frame scientific research.
When SARS-CoV-2 emerged, since most researchers are competent enough through their past research to have a good idea of how pandemics spread in general, they skipped the stage of overconfidence and quickly realized how little they knew about the new virus. This is what is called “the valley of despair”.
First, the articles follow the Dunning-Kruger effect: essentially, when we’re not good at a task, we don’t know enough to accurately assess our ability. When SARS-CoV-2 emerged, since most researchers are competent enough through their past research to have a good idea