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COVID-19 spread can be tracked via rapid, large-scale wastewater surveillance system New system identifies buildings where new COVID-19 cases have emerged, even before infected people develop symptoms
Detecting outbreaks of the novel coronavirus (COVID-19) before they spread could help contain the virus, and curb new cases within a community. In a new paper published in the open-access Journal of the American Society for Microbiology, University of California San Diego (UCSD), researchers described a mostly-automated early alert tracking system that uses high-throughput analysis of wastewater samples. These samples were used to identify buildings where new COVID-19 cases have emerged, even before infected people develop symptoms. The approach is fast, cost-effective, and sensitive enough to detect a single case of COVID-19 in a building that houses nearly 500 people, according to UCSD environmental engineer and first author Smruthi Karthikeyan, PhD, who led the design of the system. "It really lets us get a handle on new outbreaks before they get worse," she said. Previous studies have shown that analysing viral concentrations in sewage can accurately predict trends in clinical diagnoses up to a week in advance. However, conventional approaches to wastewater surveillance are laborious and time-consuming, Karthikeyan noted. To get around these bottlenecks, she said, the UCSD 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, Karthikeyan said. Every morning by 10:30, 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 tell-tale genes associated with SARS-CoV-2 (COVID-19). If the sequencing reveals all three genes, the sample is classified as positive. By early afternoon, the researchers can update the data on an online dashboard that shows where new cases have emerged. Such a surveillance system is only useful if it has a quick turnaround time, Karthikeyan said, and "there's no way we could get all those samples done on the same day unless we automated". Because every step is automated, she said, the process is not vulnerable to human error. A positive test also triggers an alert that is sent to residents of the building where the sample was collected, and they test themselves using kits from a vending machine. People who then test positive are quarantined, and the process is repeated, said Karthikeyan, a postdoctoral researcher in the lab of microbiologist Rob Knight, PhD, whose research uses computational techniques to study the ecosystems of the body. After a pilot experiment showed that the system reliably reported new cases in individual buildings, the group scaled up and began surveillance of the more than 2 million people in San Diego. "Southern California has a lot of cases, and we're bound to see it in the sewage," said Karthikeyan. In the post-pandemic future, Karthikeyan predicted a cost-effective and fast surveillance system could be particularly useful in tracking viral outbreaks in vulnerable communities with limited access to healthcare.
