Research from the lab of Fangqiong Ling at Washington University in St. Louis showed earlier this year that the amount of SARS-CoV-2 in a wastewater system was correlated with the burden of disease — COVID-19 — in the region it served.
But before that work could be done, Ling needed to know: How can you figure out the number of individuals represented in a random sample of wastewater?
A chance encounter with a colleague helped Ling, an assistant professor in the Department of Energy, Environmental and Chemical Engineering at the McKelvey School of Engineering, develop a machine learning model that used the assortment of microbes found in wastewater to tease out how many individual people they represented. Going forward, this method may be able to link other properties in wastewater to individual-level data.
The research was published in the journal PLOS Computational Biology.
The problem was straightforward: “If you just take one scoop of wastewater, you don’t know how many people you’re measuring,” Ling said. This is counter to the way studies are typically designed.
“Usually when you design your experiment, you design your sample size, you know how many people you’re measuring,” Ling said. Before she could look for a correlation between SARS-CoV-2 and the number of people with COVID, she had to figure out how many people were represented in the water she was testing.
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