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

Author(s)

Inward, Rhys P. D. ; Parag, Kris, V ; Faria, Nuno R.

• Document type: Journal article
• Source: NATURE COMMUNICATIONS; v. 13, n. 1, p. 14-pg., 2022-09-23.

Abstract

SARS-CoV-2 genome sequencing data can be used to infer epidemiological parameters, but the impact of the strategy used to select samples on these estimates is rarely considered. Here, the authors produce estimates using different sampling strategies and compare results to those based on case reporting 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. (AU)

• FAPESP's process: 18/14389-0 - Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE)
• Grantee: Ester Cerdeira Sabino
• Support Opportunities: Research Projects - Thematic Grants