By Megan May
Employing wastewater epidemiology — proven useful in outbreaks of polio and opioid use — UNC microbiologist Rachel Noble is leading a state-wide collaboration tracking novel coronavirus outbreaks across North Carolina, gaining insight that testing individuals does not offer. Preliminary results have shown that by using wastewater, researchers can identify COVID-19 hot spots five to seven days before they are reflected by clinical testing results.
UNC research technician Tom Clerkin and graduate student Mark Ciesielski turn off a gravel road and into the Beaufort Wastewater Treatment Plant. They jump out of their truck, greeted by the scorching sun and an aroma that is so unique to these types of facilities. Grabbing armloads of equipment, they climb atop the wastewater intake platform, where incoming sewage is cleared of large debris. They don full protective gear including face masks, gloves, and face shields, and collect their weekly samples. The work is far from glamorous, but serves an important purpose.
The team, led by UNC Institute of Marine Sciences microbiologist Rachel Noble, is quantifying COVID-19 concentrations in communities throughout North Carolina. While testing an individual is useful for the person who receives it, the people they come into contact with, and official coronavirus case counts, it doesn’t provide information about the number of asymptomatic people not tested — which experts believe is significant.
Noble expects their data will help account for asymptomatic carriers, identify hot spots, and inform public health measures like school policies and face mask mandates. As of August, preliminary results have shown an increase of the virus in wastewater about five to seven days before spikes in clinical cases — leading researchers to think results could serve as a sentinel for community spread.
The key, Noble says, is to focus on the concentration of the virus throughout the population.
“We’re not just looking for the positive or negative result of the virus in the wastewater samples,” she says. “What we’re looking for are the trends of whether the numbers are increasing or decreasing, and we’re particularly looking for the time whenever the signal disappears.”