American Scientist

Getting a handle on the COVID-19 pandemic requires interdisciplinary collaboration, but processing the different approaches, viewpoints, and data presents its own challenges. Enter Shweta Bansal, whose work in infectious disease modeling is crucial to understanding and predicting the spread of the pandemic. Bansal is a provost’s distinguished associate professor of biology at Georgetown University whose research in the field of interdisciplinary mathematical biology has focused on the development of data-driven mathematical models for the prevention and containment of human and animal infectious diseases. She and her team have repurposed alternative sources of data—such as healthcare transactions, social media posts, and GPS from mobile phones—to track the pandemic with a level of demographic and geographic specificity not available via traditional avenues. In October 2020, Bansal presented her findings as part the Sigma Xi Virtual COVID-19 Distinguished Lectureship Series, and she spoke with neurophysiologist and science communicator Kiki Sanford about the implications of her research. An excerpt of their discussion is below, and a full video of the presentation follows.

How has infectious disease modeling informed our understanding of COVID-19’s effect on the U.S. population?

The impact of COVID on individuals has been underestimated. Often it’s communicated in a way where it seems like less of a threat than it truly is. Yes, the range of symptoms, the range of clinical disease that patients with COVID experience is quite broad compared to something like the flu. And surely there are individuals that experience mild disease. But there’s also a really wide range of severe outcomes. There are systemic issues that COVID can cause—neurological concerns, heart issues, and kidney problems. And it can of course lead to death.

But in addition to that, one really dangerous aspect is what’s being called long COVID. Certain individuals who are often quite healthy before getting this infection are experiencing symptoms months later. At the moment we don’t have a great estimate of how common long COVID is, but in the small data sets that are out there, it has been found in a significant way. And it’s not just affecting older or immunocompromised individuals. There is a lot of damage that this virus does. But that is not an understanding that everyone has.

My team has been thinking about how your local information environment influences your behavior. My hypothesis is that, if you are in an area where you are not observing many detrimental impacts, or if you don’t know somebody who has died from COVID, then that decreases your risk perception and affects your behavior. This is something we’re trying to quantify to better understand that process.

If that is the case, then it means we need better communication, especially in areas where COVID hasn’t been very prevalent, because of course there’s a feedback loop. If COVID hasn’t been prevalent, people’s risk perception is low, which means they don’t engage in the right behaviors, which means COVID will be prevalent in that location. If we can get ahead of the curve by using the behavior and risk perception understanding to get a handle on where it might be hitting next, that would be a huge win.

Do you believe social media is more beneficial or more destructive when it comes to the public’s perception of data and information on issues such as COVID-19?

I would say it’s a mixed bag with social media. There’s the use of social media within data analysis and modeling studies themselves. A lot of my fellow modelers have used social media data to, for example, understand where disease might be. The idea is that somebody tweeting “I have a cough” gets translated by the model into, “This person might have the flu.”

What I think is a better use of social media data is looking at how people are perceiving risks and what they’re doing about it. That’s something my team has done, especially with our vaccination work, to get a handle on how much vaccine confidence there is in the population measured through social media data.

Another aspect is how social media contributes to our communication about this virus. I would say it has really expanded who is getting information and data on infectious diseases—on COVID—and how quickly that’s happening. All of that has been for the positive. That has been a real shift in how the American public interacts with data on infectious diseases.

But, of course, misinformation is a problem that is central to our times. And I can’t say that this field is by any means immune to that. I’m happy that we’re seeing private companies step up and try to take some actions towards reducing misinformation. Twitter recently changed their rules around liking and retweeting so that misinformation is reduced. Clearly this is a problem we’re going to need to keep solving, but I think overall social media is helping information dissemination in a big way.

What were some of the errors that were made that you have been able to identify, and how do you think we can do better there?

One error that comes to mind, which I think really changed the course of how we have experienced this crisis, is the lack at a national level of an integrated and coordinated response. Public health departments at the local and state level can only do so much. Honestly, they’re really only responsible for the lives and the health of those who live in their state. The United States is not only large, but it’s also spatially heterogeneous, which requires a coordinated response at the national level.

Two other errors have to do with intervention. In most cases, the response came quite late. Even if a state instituted a full-fledged lockdown, if it came just a few days late, in some cases that made huge differences in where we see the pandemic today.

The other problem is that we didn’t keep the interventions going long enough. Summer (2020) came, and we all had been doing our best for a few months at that point, and we wanted a prize for being so good for so long. There was pressure from businesses and pressure from individuals. Most states lifted their lockdown measures too early. And unfortunately, that meant that we kept going back into the infection cycle. The nature of lockdowns and surges in disease cases comes from the fact that if you don’t keep those lockdown measures in place and in a strong way long enough, and you don’t do them early enough, then we’re going to be stuck in this cycle.