Yale Daily News

Scientists at the Yale School of Engineering and Applied Science have recently released a website that tracks the spread of COVID-19 using data collected from wastewater samples.

Over the summer, a team of researchers from professor of chemical and environmental engineering Jordan Peccia’s lab developed a method for detecting SARS-CoV-2 in sewage samples that could serve as a predictive measure for future COVID-19 outbreaks. This fall, Peccia, Alessandro Zulli GRD ’26 and Annabelle Pan ’20 created a public-facing website that uses both data provided by the Connecticut Department of Public Health and SARS-CoV-2 wastewater data to monitor the spread of the virus in real time. The website provides a detailed report of COVID-19 epidemiological trends in municipalities throughout Connecticut.

“The more information the public is made aware of, and the sooner they have access to it, the more likely they can take the necessary precautions,” Zulli wrote in an email to the News.

Peccia said that his lab had two main goals for the wastewater model and corresponding website — to provide an up-to-date resource for Connecticut and to develop the technology that will serve as the foundation for wastewater-based epidemiology methods in future epidemics.

Pan also spoke to the importance of using wastewater data as an epidemiological tool.

“Tracking wastewater isn’t always the most glamorous thing in practice!” Pan wrote. “But when it comes down to it, it can be one of the most accurate and cost-effective ways of measuring disease prevalence in a community, and that is truly useful –– not just in this particular Covid-19 pandemic but also for outbreaks that happen in the future.”

Zulli explained that the wastewater model works by examining “primary sludge,” or samples of sewage that are obtained directly from wastewater processing plants. The researchers then analyze the concentration of COVID-19 RNA — a molecule that contains the virus’s genetic information — per millimeter of sludge to get an understanding of viral trends in communities throughout the state.

He said that this model, in addition to testing and contact tracing methods, is incredibly valuable to the public. This is because wastewater data can predict the trends in testing data a week in advance, making it a powerful tool for public health officials and the general population.

“A rise in RNA in wastewater allows health officials to preemptively prepare resources for an increase in cases,” Zulli wrote.

Additionally, he wrote that the wastewater data displayed on the website helps officials compare relative trends in nearby cities to track how much viral transfer there is between municipalities, such as the level of viral spread between Bridgeport and New Haven.

Zulli mentioned several challenges in developing the wastewater model and website. One such challenge was figuring out how to broadcast a model that was both scientifically robust and general enough to be applicable across many municipalities.

Pan wrote that the team is continuing to develop a “robust” quantitative method to differentiate between when spikes in RNA concentration levels are indications of likely outbreaks versus when they simply represent “noise” in the data.

“In the early stages of an outbreak, there can be a lot of subjectivity around whether something ‘looks’ concerning,” Pan wrote. “So having a solid quantitative framework of what really ‘is’ concerning might help guide consistent and effective policy.”

Despite this potential subjectivity, Pan mentioned that the data of RNA isolated from sewage samples from various municipalities has so far largely paralleled real outbreaks in these areas.

Using the data available on the website, Zulli described several concerning trends from the last couple of weeks. He noted that the team has observed increases in RNA measurements across five of the six Connecticut municipalities they are monitoring.

“New Haven [RNA measurements] ha[ve] increased particularly quickly,” Zulli wrote. “People should keep an eye out for health officials guidance, along with taking more precautionary measures in general.”

Department Chair of Epidemiology of Microbial Disease at the Yale School of Public Health Albert Ko also commented on the model. He said the public nature of the data is crucial, as it helps Connecticut residents understand precipitating events leading to outbreaks and how well COVID-19 interventions are working in communities in real time.

“The name of the game for COVID is to get ahead of the curve and not behind the curve,” Ko said.

The Peccia Lab is located at 9 Hillhouse Ave.

Sydney Gray | sydney.gray@yale.edu 

Correction, Nov. 20: An earlier version of this story said that the researchers were from the Yale School of Public Health. In fact, they are from the Yale School of Engineering and Applied Science. The story has been updated.