Enhanced Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater


Wastewater-based epidemiology has emerged as a powerful tool for public health response to the SARS-CoV-2 pandemic. As wastewater is a pooled, community sample of all persons contributing to the waste stream, there are several challenges in using sequencing information from wastewater samples to detect variants. Wastewater typically will consist of fragmented genomes from multiple, circulating variants. While it is straightforward to call the mutations present in a wastewater sample, it is more challenging to call the presence of variants that are defined by a set of characteristic mutations, particularly when mutations are shared among many circulating variants. Hence, we present a novel approach for screening for variants of concern in wastewater. Our computational approach introduces the concept of a “quasi-unique mutation” corresponding to a given PANGO lineage. We show that our method enables detection of the emergence of variants of concern in communities, providing a new approach for wastewater-based epidemiology of SARS-CoV-2.

Nicolae Sapoval
Nicolae Sapoval
PhD student

Nick (3rd year PhD student) obtained a B.S. degree in Computer Science and a B.S. with Honors in Mathematics from the University of Chicago. At the University of Chicago Nick worked in wireless networks research and later in computational biophysics focusing on conformational transition modeling for insulin degrading enzyme. His current interests are in the areas of computational biology with a focus on genomic data.