A team of infectious disease experts and engineers at Columbia University have received a $4.7 million grant from the NIH to investigate the use of urban wastewater as a cost-effective and rapid means to detect new outbreaks of SARS-CoV-2.
The project is led by Anne-Catrin Uhlemann, MD, associate professor of medicine in the Vagelos College of Physicians and Surgeons, and Kartik Chandran, PhD, professor of environmental engineering in the School of Engineering.
One of the painful lessons of the COVID-19 pandemic was the nation's inability to determine the location and prevalence of the virus quickly and accurately. Testing of individuals provided some data, but the process was slow, incomplete, and piecemeal and the data were typically 7-10 days behind the actual spread of the virus.
Wastewater surveillance, by contrast, would enable population-level monitoring, would allow for earlier detection of acute outbreaks, and would overcome hurdles to individual testing such as stigma and lack of access.
The term "domestic wastewater" refers to human urine and feces and other bodily streams in combination with water that has been used for washing, flushing, bathing or cooking- essentially anything that flows down the toilet or sink. In this project, Uhlemann and Chandran will test wastewater from the dormitories, classrooms, laboratories, and medical facilities of Columbia University.
Though wastewater testing has great promise, the best methods and protocols and the most effective ways to utilize the data are still being developed.
Though wastewater testing has great promise, the best methods and protocols and the most effective ways to utilize the data are still being developed. This study will answer the following critical questions: How do the levels and diversity of the virus in wastewater from a single building or an entire campus correlate to the prevalence of coronavirus in the population encompassed by the testing? What are the most accurate water testing technologies? And how can the data be made available and interpreted in real time?
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The project is supported by NIH grant 1U01DA053949-01.
