SARS-CoV-2 VARIANT PREVALENCE ESTIMATION USING WASTEWATER SAMPLES

Abstract The present work describes a statistical model to account for sequencing information of SARS-CoV-2 variants in wastewater samples. The model expresses the joint probability distribution of the number of genomic reads corresponding to mutations and non-mutations in every locus in terms of the variant proportions and the joint mutation distribution within every variant. Since the variant joint mutation distribution can be estimated using GISAID data, the only unknown parameters in the model are the variant proportions. These are estimated using maximum likelihood. The method is applied to monitor the evolution of variant proportions using genomic data coming from wastewater samples collected in A Coruña (NW Spain) in the period May 2021 – March 2022. Although the procedure is applied assuming independence among the number of reads along the genome, it is also extended to account for Markovian dependence of counts along loci in the aggregated information coming from wastewater samples.