Demarcating science in the midst of the COVID-19 crisis
By Maria Eugenia Inda, EECS Communication Fellow
It matters enormously if I alienate anyone from the truth. — C. S. Lewis
In 2020, life was crushed by an invisible enemy that stopped everything… or rather, almost everything, since people’s fears, worries, and questions have only grown. Unfortunately, COVID-19 asks the general public to sit patiently on the couch as spectators while scientists work frantically to find answers. As scientists, we’ve learned first-hand that science is an iterative, self-correcting process, but the public is now seeing it happen in real time. That uncertainty can be very scary.
There is a big gap between how scientists gather information and how the general public reads it. Given the general distrust of information in the post-truth era1 and the serious, fast-moving situation in which we are immersed, scientists must work to satisfy the public’s twin expectations of competence and clear communication in our research. It is our responsibility as technical experts to help our communities understand what they can realistically expect from science. That’s a public service.
Here are some strategies that we can use to clarify uncertainty as scientists and doctors work to improve understanding and treatment of COVID-19. Overall, remember that public skepticism of science is not due to a lack of scientific knowledge2, but a lack of trust. Rather than fill the information deficit, science communicators need to gain the audience’s credibility to be effective. Whenever possible, emphasize values and experiences that you share with them and focus on how the science affects their lives.
Communicate where we are in the process of reaching scientific consensus.
The process of building scientific consensus has been described as puzzle-solving3. With the novel coronavirus, scientists are trying to solve the puzzle using our previous understanding of related viruses and fast-incoming new data. Competing theories and new findings can contradict the emerging paradigm of understanding, challenging and refining it. If a paradigm is unsalvageable, we might need to start all over again.
All this may appear chaotic to the general public, who just want to see a solution that will allow them to return to normal life. People unfamiliar with this process may fall into the magical thinking that science has a solution just around the corner. If you’re an expert on any COVID-19-related research, it’s important to set realistic expectations by communicating where we are in the process of understanding and treating this new virus.
Preprints, computer models, and animal models: Please handle with care.
1. Preprints are scientific papers that have not yet been formally peer-reviewed. Normally they allow authors to receive early feedback before submission for publication. Because they are shared within a scholarly community, the intended audience is well-equipped to gauge the reliability of new claims. Since the outbreak began, COVID-19 preprints have proliferated, allowing fast access to new findings. However, since these haven’t been subjected to the full review process, avoid citing them without appropriate warnings to your audience.
2. A computer model is not a prophecy, but a tool. They help scientists simulate complex situations like COVID-19’s spread, helping to build hypotheses – which then need to be validated by further data. The math behind the models can be continually updated with new data, too.
Dr. Anthony S. Fauci, MD, Director of the National Institute of Allergy and Infectious Diseases (NIAID), pointed out:
“When someone creates a model they put in various assumptions, and the model is only as good and as accurate as your assumptions.”
3. Drugs and tests on experimental animal models may have different outcomes in humans because of the complexity of biological systems. Even within humans, a drug that’s effective in one population may have different effects in another group 4. That’s why extended clinical trials are necessary beyond the initial safety tests in vaccine development, so as to assess the effect on diverse patients under real risk of infection, Fauci explained.
In sum: from peer review to clinical trials, science establishes many checkpoints to help us work towards a reliable scientific consensus. However, advancing through those checkpoints takes time, hard work, and many resources. COVID-19 research is going high-speed, but we need to remind the public that we cannot take shortcuts.
Beware of “scientism:” Don’t use science to answer questions that science can’t answer.
When most people talk about fatality rates, what they’re thinking is, “If I get this disease, will I die?” This is beyond human knowledge. The best answer we can give is a probability: the current fatality rate estimate, which is itself uncertain (see previous blog post).
To speak with scientific authority, we need to use fact-based arguments. “Why is COVID-19 a health threat for humans and not for dogs?” is a question within the scope of the scientific method. However, “Why is this happening now to all of humanity?” is a question to which we can’t apply the scientific method.
Scientists, like all humans, have their own beliefs. But we need to draw a clear distinction when introducing non-scientific assumptions, extrapolations, philosophy, or metaphysics based on personal beliefs. Otherwise, we’ll fall into the error of scientism, the view that science is the only source of real knowledge. Scientism is responsible for much of the modern suspicion of science by large sections of society, since it suffocates reason and can threaten their values2. Acknowledging science’s limitations liberates both science and other approaches to understanding the world.
Caution: Science in progress
Science will lose its credibility if we cannot draw a clear distinction between what is known to be true from what is thought to be true — and what is speculation or opinion. In the COVID-19 crisis, there’s still a lot of research to do, and it’s prudent not to guesstimate. As scientists, we hold the responsibility to communicate what we do know, and be humble about what we don’t.
 Keyes, R (2004). The Post-Truth Era: Dishonesty and Deception in Contemporary Life. Macmillan.
 Kahan, D., Peters, E., Wittlin, M. et al (2012). The polarizing impact of science literacy and numeracy on perceived climate change risks. Nature Clim Change 2, 732–735.
 Kuhn T. S. (1962). The Structure of Scientific Revolutions p. 35-42.
 Currie, G. P., Lee, D. K. & Lipworth, B. J. (2006). Long-Acting β2-Agonists in Asthma. Drug Saf. 29, 647–656.
Read the rest of the Communication Lab’s COVID-19 blog series
- How to Be Clear When Nothing Else Is, pt. I: Communicating Science During a Pandemic, by Sarah Schwartz
- How to Be Clear When Nothing Else Is, pt. II: Reading Responsibly During a Pandemic, by Sarah Schwartz
- Let’s Not Make it Viral! Help Flatten the Curve of Misinformation, by Eugenia Inda
- A Designer’s Perspective on Data Visualization: Strategies Scientists Can Use When Creating Graphics for a Broader Audience, by Craig McLean, Yerri Portillo, Tianna Rivera, Tui Calvette, and Christine Lopez