AI4Pandemics Talk #24: Jiayang Li, The University of Queensland
Title: SARS-CoV-2 in sewer networks: tracking the COVID-19 spread in the community
Abstract: Monitoring SARS-CoV-2 RNA in sewer systems is an effective approach for understanding COVID-19 transmission in communities with higher spatial resolutions.
Wastewater surveillance for SARS-CoV-2 has been rapidly developed worldwide for monitoring the COVID-19 prevalence at the population level. Sampling in sewage networks, i.e., the upstream of a wastewater treatment plant, is proposed to understand community transmission. Passive samplers are cost-effective and suitable for sewer networks or catchments where autosamplers cannot be operated. This presentation will tell a full story about how we trace the SARS-CoV-2 in sewer networks: 1) how passive samplers are calibrated to provide time-integrative information, 2) how to determine passive samplers’ sensitivity for low COVID-19 cases in an area, and 3) how to apply passive samplers for upstream monitoring to identify the emergence and dynamics of COVID-19 in communities. These findings demonstrate the ability of upstream wastewater surveillance for identifying SARS-CoV-2 in low-case settings and tracking COVID-19 spread in communities with higher spatial resolutions.
About AI4PAN Artificial Intelligence for Pandemics Seminar Series centred at UQ
Welcome to AI4PAN, the Artificial Intelligence for Pandemics group centered at The University of Queensland (UQ). The group's focus is the application of data science, machine learning, statistical learning, applied mathematics, computation, and other "artificial intelligence" techniques for managing pandemics both at the epidemic and clinical level.