As the COVID-19 pandemic proliferated in early 2020, tracking the spread of a frequently asymptomatic virus presented a novel challenge for public health officials. Diagnostic testing was often unavailable and not fast enough to act as a useful forecasting tool.
While symptoms can take several days to appear, the virus immediately begins replicating in the gastrointestinal tract, and traces of it end up in the infected individuals’ stool. Researchers at the University of California San Diego (UC San Diego) quickly determined that testing sewage for viral markers would allow for earlier detection of infections and potential outbreaks.
The wastewater sampling initiative began as a pilot program on the UC San Diego campus in the summer of 2020 with only six collection sites but quickly grew as they determined that environmental testing was a key to preventing the spread of COVID-19. However, the manual work involved in concentrating samples was a roadblock to scaling the surveillance program beyond the university.
“Viral concentration is typically the biggest bottleneck in wastewater analyses since it’s laborious and very time-consuming. Here, we optimized that step via automation with liquid-handling robots,” said Smruthi Karthikeyan, a postdoctoral researcher at UC San Diego School of Medicine. The automated system not only increases the number of samples that can be processed concurrently – 24 in 40 minutes – it also removes the potential for human error and accelerates the timeline from the collection of samples to viral detection, enabling a more rapid response to positive results.
This project marked the first collaboration between the Center for Microbiome Innovation (CMI) and the Center for Machine-Integrated Computing & Security (CMICS), both research centers are part of UC San Diego Jacobs School of Engineering. The system is described in a new paper entitled “High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County”, published March 2, 2021, in mSystems.
As a result of the increased scale at which wastewater samples were tested, the team could track viral detection patterns from a far higher-level perspective than before. “In this paper, we observed strong correlations and forecasting potential between viral content in daily wastewater samples from the Point Loma wastewater treatment plant and positive COVID-19 cases reported for San Diego county when modeled with temporal information,” according to Nancy Ronquillo, a graduate student researcher with the CMICS.
While the system is already enabling health officials to forecast the spread of COVID-19 with greater speed and accuracy, efforts continue to expand what data can be extracted from wastewater. Karthikeyan noted that performing viral genome sequencing of the samples could help track the prevalence of different variants of SARS-CoV-2 and model the impact they’re having on viral spread throughout the community. On a more granular level, the team is studying the dynamics of viral shedding and transmission at UC San Diego, which will ultimately aid the university in safely reopening the campus under the Return to Learn program.
“The Center for Microbiome Innovation and Center for Machine-Integrated Computing & Security collaboration accelerated the trace viral pathogen detection modeling in large-scale wastewater sampling throughout San Diego,” remarked Andrew Bartko, executive director of the CMI. “We’re grateful to contribute to this interdisciplinary team and generate impactful developments in the fight against COVID-19. We look forward to leveraging our complementary technologies and expertise in the future.”
Additional co-authors include Pedro Belda-Ferre, Destiny Alvarado, Tara Javidi, Christopher A. Longhurst, and Rob Knight, all at UC San Diego.
The Center for Microbiome Innovation is proud to include Rob Knight on its leadership team.
This piece was written by CMI’s contributing editor Cassidy Symons
