Canadian PI: Dr. Ajay Ray
Canadian Institution: Western University
Indian PI: Dr. Sridharakumar Narasimhan
Indian Institution: Indian Institute of Technology (IIT) Madras
Project Summary:
Wastewater-based epidemiology (WBE) has been proven as a very effective tool for implementing rapid response to pandemic events including dynamic social-distancing policies. However, WBE calculations are potentially affected by large errors due to (inaccurate) assumptions on the fate and transport of pathogens in sewers systems. Indeed, while detection of genetic material in sewer systems could be sufficient for qualitative estimates, qualitative predictions require a greater understanding of pathogens-sewer interactions as a function of sewer-specific factors such as temperature, redox, pH, sewer chemicals (i.e., iron, nitrates, etc.), shear, sediments, pipe velocity, and so on. Thus, to develop next generation WBE-based epidemiological models, such obvious knowledge gaps must be promptly filled.
In this project, researchers plan to conduct sewer pilot and modeling studies in Canada and India, respectively. To derive transport properties for several types of microorganisms, and conditions, a scaled-down sewer pilot system and machine-learning algorithms will be used. Specifically, leveraging the strong expertise available at the state-of-the-art facility accessible at Western University (ImPaKT, CL2+/CL3 facility able to work with human pathogens too) via our collaborators, we plan to conduct pilot studies to characterize the transport of several indigenous pathogens (fecal coliforms, DNA/RNA viruses, etc.) and surrogates including bacteriophages (MS2, T1UV, T7, Q-Beta, Φ174), bacillus-like organisms (Bacillus subtilis, Bacillus pumilus), fungi (Aspergillus brasiliensis, Trichoderma harzianum) and coliform species (E. coli). Over 1,000 samples will be collected and enumerated under a variety of sewer process conditions.
Pilot data will then be shared with the Indian team who will be responsible for the development of next generation epidemiological models based on the integration, using machine-learning algorithms, of pathogen-calibrated sewer simulations with classical compartmental epidemiological models (SIR, SEIR, SAPHIRE, ICM, etc.). Pathogen-specific subroutines will be integrated in EPA-SWMM and Mega-WATS® for the prediction of pathogen dynamics and hotspots in city-wide sewer systems. Finally, next generation of AI-based epidemiological models will be derived for day-ahead predictions of future pandemic waves.
