A group of investigators at Duke University is embarking on an innovative research study that will draw upon expertise and insight from across campus to detect active, undiagnosed cases of coronavirus infection and shed light on how the SARS-CoV-2 coronavirus spreads through communities in Durham, North Carolina.
The study, called “Snowball” for short (the full name is “Duke RDS2: Respondent-Driven Sampling for Respiratory Disease Surveillance, the Snowball Sampling Study”), officially opened to enrollment on Friday, December 11. While in some ways similar to the contact tracing efforts that have become familiar during the COVID-19 pandemic, Snowball takes a unique approach to harnessing each study participant’s knowledge and understanding of their own environment and relationships to help identify others within their own social spheres who might be at risk.
Supported by a contract with the U.S. Centers for Disease Control and Prevention (CDC), the Snowball study is drawing on expertise from across Duke campus, including the Pratt School of Engineering and the School of Medicine. It’s also teaming up with two other major COVID-19 research projects already underway at Duke University: the first, the Molecular and Epidemiological Study of Suspected Infection (MESSI) study taking place at the Duke Center for Applied Genomics and Precision Medicine, will apply sophisticated molecular and genomic testing to study samples from participants with active SARS-CoV-2 infections. The second, the Duke CovIdentify study headed by Big Ideas Lab Director Jessilyn Dunn, PhD, will supply Snowball participants with wearable electronic devices that allow researchers to analyze biometric readings such as temperature and heart and respiration rate.
Here’s how Snowball works: when an adult Durham County resident tests positive for the SARS-CoV-2 virus at a Duke University Health System hospital or clinic, they can be offered the opportunity to participate in Snowball. If they consent to participate, they will fill out a questionnaire and be given a set of 3 to 5 electronic “coupons” for free coronavirus testing, either at a Duke clinic or at the participant’s home. These coupons, which are similar to a social media “friend” request, can then be given out by the study participant (known as a “seed”) to people they live, work, or socialize with—in other words, people with whom the seed has been in relatively close contact, or who go to the same places as the seed.
Each contact who tests positive will be offered the opportunity to participate in the Snowball study and given their own set of coupons to help identify other members of their own social network who may be at risk for infection. This creates a self-sustaining chain reaction of recruitment into the study. Snowball gets its name from this approach to identifying and recruiting study participants, which is sometimes called snowball sampling. Also known as respondent-driven sampling or network-driven sampling, the method has been successfully employed in other public health contexts involving infectious diseases, including HIV-AIDS.
“What Snowball lets us do is use each person’s own social networks as a tool for efficiently sampling the larger community,” says study principal investigator Dana Pasquale, PhD. “The basic idea is that no one knows your own social context better than you do, so why not try to use that expert knowledge to trace the possible movement of a disease that we know spreads most readily in situations where people are close together for extended periods?”
Pasquale, a Duke epidemiologist whose work has focused on social factors that affect the spread of HIV-AIDS and other sexually transmitted diseases, notes that the study’s approach comes with several important benefits.
“The basic idea is that no one knows your own social context better than you do, so why not try to use that expert knowledge to trace the possible movement of a disease that we know spreads most readily in situations where people are close together for extended periods?”
“This kind of sampling lets us look for COVID cases where they’re most likely to occur and then to understand how, and how fast, the disease is moving through a given community,” says Pasquale, who notes that this, in turn, allows health workers to break the chain of disease transmission by identifying cases before they become infectious to other members of their community. She also notes that the study’s advantages apply to surveillance for other viral respiratory diseases, such as seasonal flu.
But while the Snowball study relies on a relatively straightforward application of tried-and-true public health methods combined with a more robust social contact module (her mentor is Snowball co-investigator Jim Moody, PhD, Professor of Sociology at Duke and an expert in social network analysis), there is also a sophisticated network of research, technology and know-how powering the study as well – a network that encompasses complementary work already underway at Duke, along with expertise and insights drawn from across the campus. For example, the creation and management of the electronic coupons central to the Snowball study will take place through a data science platform created by a team at Duke Crucible headed by study co-investigator and Crucible & Duke Forge Director Erich Huang, MD, PhD.
Multiple streams of data—biomolecular and genetic analyses from MESSI; biometric measurements gathered through CovIdentify—can be combined with clinical data, survey entries, and information from participants’ symptom diaries to yield a richly detailed picture of the variety of COVID symptoms and the clinical course the disease takes in different people. These data may also shed more light on the ways the disease spreads among individuals and through communities, as well as revealing new biological markers of infection.
“What’s especially exciting about the Snowball study is not just that it lets us quickly and efficiently find people who may be at risk for developing COVID-19 and steer them toward the help and services they may need, but it also lets us explore the underlying biology of the disease at the same time,” notes MESSI principal investigator Christopher W. Woods, MD, MPH, who is also a co-investigator for the Snowball study.
Another facet of the COVID-19 pandemic that Snowball will examine is the role that socioeconomic factors play in disease risk and in accessing needed care and resources. Co-investigator and Director of the Health Equity Working Group at the Cook Center on Social Equity, Keisha Bentley-Edwards, PhD, will lead efforts to develop measurements that can shed light on the social determinants of health that may affect how individual and communities are impacted by the pandemic.
Ultimately, Pasquale hopes that the experience with Snowball study will yield lessons that can be adapted and scaled to help fight the COVID-19 pandemic beyond Durham.
“We’ve managed to assemble a great team, one that lets us look at the problem of COVID infection and transmission through a really wide lens,” Pasquale notes. “Instead of just focusing on isolated details, we have the chance to see the whole picture of COVID transmission at once—from immunology and genomics all the way to the social contexts in which people encounter this disease.”