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Vaccine escape in a heterogeneous population: insights for SARS-CoV-2 from a simple model

  • Julia R. Gog*
  • , Edward M. Hill
  • , Leon Danon
  • , Robin N. Thompson
  • *Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

31 Citations (Scopus)

Abstract

As a countermeasure to the SARS-CoV-2 pandemic, there has been swift development and clinical trial assessment of candidate vaccines, with subsequent deployment as part of mass vaccination campaigns. However, the SARS-CoV-2 virus has demonstrated the ability to mutate and develop variants, which can modify epidemiological properties and potentially also the effectiveness of vaccines. The widespread deployment of highly effective vaccines may rapidly exert selection pressure on the SARS-CoV-2 virus directed towards mutations that escape the vaccine-induced immune response. This is particularly concerning while infection is widespread. By developing and analysing a mathematical model of two population groupings with differing vulnerability and contact rates, we explore the impact of the deployment of vaccines among the population on the reproduction ratio, cases, disease abundance and vaccine escape pressure. The results from this model illustrate two insights: (i) vaccination aimed at reducing prevalence could be more effective at reducing disease than directly vaccinating the vulnerable; (ii) the highest risk for vaccine escape can occur at intermediate levels of vaccination. This work demonstrates a key principle: the careful targeting of vaccines towards particular population groups could reduce disease as much as possible while limiting the risk of vaccine escape.
Original languageEnglish
Article number210530
Number of pages15
JournalRoyal Society Open Science
Volume8
Issue number7
DOIs
Publication statusPublished - 14 Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Research Groups and Themes

  • Engineering Mathematics Research Group

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