Inferring effects of mutations on SARS-CoV-2 transmission from genomic surveillance data

Area of Science:

• Virology
• Epidemiology
• Genomic Surveillance

Background:

• New SARS-CoV-2 variants with increased transmissibility frequently emerge.
• Identifying transmission-altering mutations is crucial for understanding viral evolution and pandemic response.

Purpose of the Study:

• To develop a generalizable analytical epidemiological model to infer transmission effects of mutations from genomic surveillance data.
• To apply this model to SARS-CoV-2 data for identifying key mutations and early-stage variants.

Main Methods:

• Development of a generic analytical epidemiological model.
• Application of the model to diverse SARS-CoV-2 genomic surveillance datasets across multiple regions.
• Inference of mutation-specific transmission rate effects.

Main Results:

• Identified multiple mutations, both within and outside the Spike protein, that significantly affect SARS-CoV-2 transmission rates.
• Validated inferred high-impact mutations with existing experimental evidence.
• Demonstrated the model's capability to detect variants with increased transmission at low frequencies (e.g., Alpha, Delta, Omicron at 1-2% prevalence).

Conclusions:

• The developed model effectively infers transmission impacts of mutations from genomic data.
• This approach facilitates the rapid identification of novel, high-transmission SARS-CoV-2 variants and mutations.
• Early detection of variants aids in proactive public health strategies and understanding viral dynamics.