Researchers from the University of California San Diego carried out a study to describe a mostly automated early alert system that uses high-throughput analysis of wastewater samples.
This system helps in identifying buildings where new Covid-19 cases have emerged–even before infected people develop symptoms.
The research was published in the mSystems, an open-access Journal of the American Society for Microbiology,
“This approach is fast, cost-effective, and sensitive enough to detect a single case of COVID-19 in a building that houses nearly 500 people,” said UC San Diego environmental engineer and first author Smruthi Karthikeyan.
“It really lets us get a handle on new outbreaks before they get worse,” she said.
Earlier studies have also demonstrated that analysing viral concentrations in sewage can accurately predict trends in clinical diagnoses up to a week in advance.
Karthikeyan revealed that the team designed a system from scratch that automates most of the analysis. Automation makes it possible to get results quickly, and the system is already watching for community outbreaks in San Diego.
For the study, every morning by 10:30 am, UCSD researchers collect wastewater samples from nearly 100 stations, representing every building on campus and a local hospital. Back at the lab, a robotic platform can process 24 samples in just 40 minutes.
Then an automated, high-throughput tool extracts RNA from the samples and uses PCR to search for three telltale genes associated with SARS-CoV-2.
If the sequencing reveals all three genes, the sample is classified as positive. By early afternoon, the researchers updated the data on an online dashboard that showed were new cases had emerged.
Such a surveillance system is only useful if it has a quick turnaround time, Karthikeyan said.
She added: “There’s no way we could get all those samples done on the same day unless we automated and because every step is automated, the process isn’t vulnerable to human error.”