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

Wan Yang,Jeffrey Shaman

doi:10.1038/s41467-021-25913-9

Abstract

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.

Highlights

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
We develop a model-inference system to estimate the relative change in transmissibility and level of immune evasion for different SARS-CoV-2 variants, while accounting for under-detection of infection, delays of reporting, disease seasonality, non-pharmaceutical interventions (NPIs), and vaccination
Our estimates for the B.1.1.7 variant are consistent with detailed epidemiological evidence (32.5–54.6% increase in transmissibility and minimal immune evasion estimated here vs. 30–50% increase in secondary attack rate based on contact tracing data[3,4] and little immune evasion based on laboratory and realworld vaccination data5,6,15)

Summary

Introduction

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. We apply the validated inference system in conjunction with incidence and mortality data from the UK, South Africa, and Brazil—the three countries where VOCs B.1.1.7, B.1.351, and P.1 were first identified, respectively—to estimate the change of transmissibility and immune evasion for the three VOCs, separately We further use these inferred findings in a multi-variant, age-structured model to simulate epidemic outcomes in a municipality like New York City (NYC) where multiple variants, including B.1.1.7, B.1.351, and P.1, have been detected

Methods

Results

Conclusion