Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data (preprint)

ABSTRACT

SARS-CoV-2 virus genomes are currently being sequenced at an unprecedented pace. The choice of viral sequences used in genetic and epidemiological analysis is important as it can induce biases that detract from the value of these rich datasets. This raises questions about how a set of sequences should be chosen for analysis, and which epidemiological parameters derived from genomic data are sensitive or robust to changes in sampling. We provide initial insights on these largely understudied problems using SARS-CoV-2 genomic sequences from Hong Kong and the Amazonas State, Brazil. We consider sampling schemes that select sequences uniformly, in proportion or reciprocally with case incidence and which simply use all available sequences (unsampled). We apply Birth-Death Skyline and Skygrowth methods to estimate the time-varying reproduction number (Rt) and growth rate (rt) under these strategies as well as related R0 and date of origin parameters. We compare these to estimates from case data derived from EpiFilter, which we use as a reference for assessing bias. We find that both Rt and rt are sensitive to changes in sampling whilst R0 and the date of origin are relatively robust. Moreover, we find that the unsampled datasets, which reflect an opportunistic sampling scheme, engender the most biased Rt and rt estimates for both our Hong Kong and Amazonas case studies. We highlight that sampling strategy choices may be an influential yet neglected component of sequencing analysis pipelines. More targeted attempts at genomic surveillance and epidemic analyses, particularly in resource-poor settings with limited sequencing capabilities, are necessary to maximise the informativeness of virus genomic datasets.