Researchers from the University of California San Diego School of Medicine developed a fast and inexpensive approach to wastewater surveillance that can predict COVID-19 cases by a week with excellent accuracy, according to a study published March 2 in mSystems.
Early in pandemic, UC San Diego postdoctoral researcher Smruthi Karthikeyan, PhD, collected local wastewater samples to predict COVID-19 outbreaks after she read that people with asymptomatic and symptomatic COVID-19 shed the virus in their stool.
Dr. Karthikeyan and her team sampled sewage water from July to November to see if they could detect the novel coronavirus. They discovered that they could, but the process was slow and labor-intensive, so they went on to develop a system for automated wastewater concentration using liquid-handling robots.
The research team compared the system to existing forecasting methods and found that it can predict COVID-19 cases in San Diego by a week with excellent accuracy, and three weeks with fair accuracy. They also found it is able to identify a single COVID-19 case in a building of about 500 people.
The system, which can process 24 samples every 40 minutes, extracts RNA from sewage samples and runs a polymerase chain reaction test to search for the novel coronavirus' signature genes. Dr. Karthikeyan then adds the data to a digital dashboard that tracks new COVID-19 cases.
The study's authors said the system is a faster, cheaper and more sensitive approach to wastewater surveillance.