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

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. We provide insights on these largely understudied problems using SARS-CoV-2 genomic sequences from Hong Kong, China, and the Amazonas State, Brazil. We consider multiple sampling schemes which were used to estimate R(t) and r(t) as well as related R(0) and date of origin parameters. We find that both R(t) and r(t) are sensitive to changes in sampling whilst R(0) and the date of origin are relatively robust. Moreover, we find that analysis using unsampled datasets result in the most biased R(t) and r(t) 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.