Date/Time
Date(s) - 28/03/2023
3:30 pm - 5:00 pm

Speaker: Devan Becker, Wilfrid Laurier University

Title: Statistical Models for the Quantification of SARS-CoV-2 Variants of Concern in Wastewater Samples

Abstract: During the COVID-19 pandemic, wastewater sampling proved to be a valuable tool to gauge the prevalence in the absence of laboratory testing and without sampling bias. We also learned that genetic variants (i.e., B.1.1.7 or Omicron) can indicate new infection waves, importation of variants, and speed of spread. Genetic variants are defined by a list of mutations relative to a reference, but wastewater samples only provide a fraction of the sequence. Statistical models are able to deconvolute the aggregate counts of mutations across sampled sequences to estimate proportions of each known variant.

 In my work, I extend these models under the assumption that the proportions vary smoothly over time and space. This approach incorporates the error structure of the counts of individual mutations when estimating the spatio-temporal trends. These models have successfully estimated the temporal trends for different locations within a city with equivalent results to clinical testing

 A parallel project involves estimating the lists of mutations for the variants themselves using unsupervised machine learning techniques. In particular, I look for mutations that tend to appear together while enforcing a smooth spatiotemporal structure. Preliminary models show concordance between the variant definitions estimated from data and the known definitions, especially the temporal trend.

Date/Time: Tuesday March 28, 2023, 3:30 – 5:00 

Location: MDCL 1115