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Rotman Insights Hub | University of Toronto - Rotman School of Management

For a faster, cheaper way to identify new COVID-19 cases, look in the sewer

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Oded Berman

It turns out that we are flushing valuable information about our COVID-19 status down the toilet, and the key to tracing new cases requires taking a closer look in our sewers. These insights come from a recent study by Oded Berman, a professor of operations management and statistics at the Rotman School of Management.

Berman and his co-investigators professors Richard Larson (from the Massachusetts Institute of Technology) and Mehdi Nourinejad (from York University) developed two approaches for quickly and accurately identifying the precise locations of COVID-19 infections within communities using samples collected from manholes. Their methods, which are outlined in a PLOS One paper that was published in October 2020, could be more cost-effective and efficient than completing individual testing for an entire community.

COVID-19 is well-known for causing respiratory complications — in the most severe cases, people might experience difficulty breathing or chest pain — but the virus also affects the gastrointestinal tract. Those infected with the virus begin shedding traces of it in their stool, even before they start exhibiting obvious respiratory symptoms.

“Many other researchers have looked at how to detect COVID-19 and gauge its impact in a community through sewage. We wanted to get specific,” explains Berman. “By sampling from manholes, we can pinpoint the precise locations of COVID-19 cases and prevent future outbreaks.”

The researchers noted that sewage flows along a defined path of pipes, and that all sewer networks have a tree-like structure — with smaller pipes feeding into larger pipes that direct sewage towards a wastewater treatment facility.

In the paper, the researchers described their strategy — an algorithm — in response to detecting traces of the virus at a wastewater treatment plant in a community that has no record of active COVID-19 cases. To find patient zero, the researchers explained that a team should sequentially test samples collected from manholes upstream from the wastewater treatment plant in the sewer network.

By sampling from manholes, we can pinpoint the precise locations of COVID-19 cases and prevent future outbreaks.

Oded Berman, Professor of Operations Management and Statistics

If the samples contained traces of the shed virus, the team would know to continue testing samples upstream from that point. (If the samples contained no traces of the virus, they could eliminate the branches upstream from there from their search.)

The researchers explained that, eventually, the testing team would find a point in the system (a manhole) where there would be traces of the virus downstream but not upstream. This would lead them to their patient zero — an individual from the household connected to this manhole had shed the virus there.

Their second algorithm addresses an alternative scenario, where there might be many infected people in a community. The researchers described using a similar testing approach to identify the main infected neighbourhood.

In their simulations — they tried their algorithms out on a sewer system for a city based in Massachusetts — it only required collecting and testing samples from four to seven manholes to identify the households or neighbourhood with infected individuals.

While their approach requires tests that yield results rapidly, they are optimistic that their strategy could be implemented in communities across Canada and the U.S.

“Right now, our thinking is ahead of the technology,” says Berman. “But we’re hearing about advances in testing, so we’re hopeful that we can get to a point where we can track the source of infections (patient zero) within a day.”


Oded Berman is the former endowed Sydney Cooper chair in Business and Technology and a former associate dean of programs at Rotman School. He has published over 250 refereed articles and has contributed to several books in his field. He is a fellow of the Institute of Operations Research, a winner of the 2012 Canadian Operations Research Society (CORS) award of merit and a winner of the Lifetime Achievement Award of the INFORMS's section on location analysis.