Wastewater-based surveillance has been developing rapidly as an important tool in efforts to combat COVID-19. Since the virus is shed in feces, monitoring virus levels in wastewater can be an advance indicator of an outbreak. This is because infected persons start shedding the virus within 24 hours of being infected, while symptoms might not appear until as much as five days later.
The surveillance data will enable researchers to estimate community infection rates and determine upward or downward trends that can inform real-time management decisions. As well, the team has developed a laboratory test that can detect COVID-19 variants without needing to fully sequence the virus.
“In partnership with the City of Saskatoon and Indigenous communities, we will gather data that will enable health officials and communities to better plan for surges in COVID-19 cases so that they can implement quarantines and other measures,” said USask toxicologist John Giesy. “Even a few days of early warning in communities can be critical to the success of these pandemic preparedness measures, especially for rapidly evolving variants.”
Results from the new study will be used by USask computer scientist Nathaniel Osgood for pandemic modelling and to inform public health decision-making and health care capacity planning, using a combination of artificial intelligence and computer simulation.
The project builds on a pilot study last year led by Giesy and funded by USask’s Global Water Futures program at the university’s Global Institute for Water Security, in partnership with the City of Saskatoon and the Saskatchewan Health Authority. The data collected during that six-month study accurately predicted new cases.
“Importantly, our approach can detect the virus from the feces of both symptomatic and asymptomatic people. Identifying the presence of asymptomatic cases is very useful since many people without symptoms are not tested for the virus,” said engineering researcher Kerry McPhedran, USask Centennial Enhancement Chair in Water Stewardship for Indigenous Communities.
In a first-of-its-kind study with Indigenous communities, the team will partner with the Indigenous Technical Services Co-operative (ITSC), which includes five First Nations Tribal Councils (Agency Chiefs Tribal Council, File Hills Qu’Appelle Tribal Council, Saskatoon Tribal Council, Touchwood Agency Tribal Council, and Yorkton Tribal Council).
The team will sample water at the Saskatoon Wastewater Treatment Plant three days a week for 27 weeks, and relay the data analysis to health authorities. For First Nations partners, weekly samples will be assessed for 27 weeks as a pilot program for wastewater lagoon systems.
“Wastewater monitoring for COVID-19 will help our member First Nations respond quickly to any positive detections in our sewage. We are hopeful the knowledge gained through this study will lead to more extended wastewater monitoring in the near future for other First Nations communities,” said ITSC Executive Director Tim Isnana.
The USask team has developed a method for measuring copies of RNA from the SARS-CoV-2 coronavirus and using this data to predict the potential for, and rates of, infection in communities. The team also includes toxicologists Markus Brinkmann and Paul Jones, program manager Yuwei Xie, engineering PhD student Mohsen Asadi, and Toxicology Centre research associates Femi Oloye and Jenna Cantin.
The methods have been validated in a study with eight other laboratories across Canada, co-ordinated by the Canadian Water Network and PHAC’s National Microbiology Laboratory (NML). Read about the study outcomes here: https://cwn-rce.ca/covid-19-wastewater-coalition/phase-1-inter-laboratory-study/
The USask team plans to participate in the second phase of the inter-laboratory initiative, which is led by the Ontario Ministry of Environment Conservation and Parks in collaboration with the NML and Ontario Clean Water Agency. The aim is to compare abilities of laboratories across Canada to achieve comparable detection of the COVID-19 virus from wastewater, providing data for pandemic modelling and public health decision-making.
Article re-posted on .
View original article.