Development of a model-inference system for estimating epidemiological characteristics of SARS-CoV-2 variants of concern

Quantification of the transmissibility and immune escape properties of SARS-CoV-2 variants is necessary to support pandemic planning. Here, the authors develop a model inference system to estimate these properties using incidence and mortality data for three variants of concern. To support COVID-19 pandemic planning, we develop a model-inference system to estimate epidemiological properties of new SARS-CoV-2 variants of concern using case and mortality data while accounting for under-ascertainment, disease seasonality, non-pharmaceutical interventions, and mass-vaccination. Applying this system to study three variants of concern, we estimate that B.1.1.7 has a 46.6% (95% CI: 32.3-54.6%) transmissibility increase but nominal immune escape from protection induced by prior wild-type infection; B.1.351 has a 32.4% (95% CI: 14.6-48.0%) transmissibility increase and 61.3% (95% CI: 42.6-85.8%) immune escape; and P.1 has a 43.3% (95% CI: 30.3-65.3%) transmissibility increase and 52.5% (95% CI: 0-75.8%) immune escape. Model simulations indicate that B.1.351 and P.1 could outcompete B.1.1.7 and lead to increased infections. Our findings highlight the importance of preventing the spread of variants of concern, via continued preventive measures, prompt mass-vaccination, continued vaccine efficacy monitoring, and possible updating of vaccine formulations to ensure high efficacy.

Automated summary

The study developed a model inference system to estimate the transmissibility and immune escape properties of three SARS-CoV-2 variants, finding that certain variants have higher transmissibility and immune escape potential, suggesting the importance of continued prevention measures to control the spread of these variants.