Thomas Maere obtained his Master’s degree in Bioengineering (Forest, Soil and Water Management) from Ghent University (Belgium) in 2006. In 2007, he was a research assistant at the BIOMATH research unit at Ghent University, working on the modelling-based optimisation of conventional wastewater treatment plants. He began his PhD in the same department on the modelling of membrane bioreactors in 2008, followed by a postdoctoral fellowship in 2012 on various membrane-related topics. Upon moving to Quebec City (Canada) in 2014, he became a postdoctoral fellow at the modelEAU research unit at Laval University and was primarily involved in the InnovaReg project on nutrient regulation for water resource recovery facilities.

Thomas Maere obtained his Master’s degree in Bioengineering (Forest, Soil and Water Management) from Ghent University (Belgium) in 2006. In 2007, he was a research assistant at the BIOMATH research unit at Ghent University, working on the modelling-based optimisation of conventional wastewater treatment plants. He began his PhD in the same department on the modelling of membrane bioreactors in 2008, followed by a postdoctoral fellowship in 2012 on various membrane-related topics. Upon moving to Quebec City (Canada) in 2014, he became a postdoctoral fellow at the modelEAU research unit at Laval University and was primarily involved in the InnovaReg project on nutrient regulation for water resource recovery facilities.

COVID-19: back-calculations for wastewater-based epidemiology using hybrid modelling methods. NSERC Alliance Grant, in collaboration with Thales Digital Solutions Inc. and the City of Québec, for the development of modelling and simulation tools to track the fate of the COVID virus in wastewater, in order to correct trends observed in the viral signal measured at the inlet of wastewater treatment plants. Monitoring of infection levels in the population served by the wastewater treatment plant under study can thus be improved, and the corrected analysis results can support crisis management. The corrections to be modelled include the effect of wastewater dilution by rain and snowmelt, residence time in the sewer network, the effect of temperature, and interaction with particles contained in the wastewater. The tasks also include the analysis of conventional epidemiological data in order to use artificial intelligence methods (‘machine learning’) to provide additional sources of information that could further improve the viral signal obtained.