The potential for vaccination-induced herd immunity against the SARS-CoV-2 B.1.1.7 variant

CMMID COVID-19 Working Group; David Hodgson; William Waites

doi:10.2807/1560-7917.ES.2021.26.20.2100428

The potential for vaccination-induced herd immunity against the SARS-CoV-2 B.1.1.7 variant

CMMID COVID-19 Working Group, David Hodgson^*, William Waites

^*Corresponding author for this work

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Abstract

We assess the feasibility of reaching the herd immunity threshold against SARS-CoV-2 through vaccination, considering vaccine effectiveness (VE), transmissibility of the virus and the level of pre-existing immunity in populations, as well as their age structure. If highly transmissible variants of concern become dominant in areas with low levels of naturally-acquired immunity and/or in populations with large proportions of < 15 year-olds, control of infection without non-pharmaceutical interventions may only be possible with a VE ≥ 80%, and coverage extended to children. Initial reports of vaccine effectiveness against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), the virus responsible for coronavirus disease (COVID-19), have suggested a substantial reduction of the risk of infection [1]. Nevertheless, with the emergence of more transmissible variants such as B.1.1.7 [2], how large-scale immunisation programmes against SARS-CoV-2 will perform is currently unclear. This study assesses the potential of COVID-19 vaccination to generate herd immunity and takes into account vaccine effectiveness, naturally-acquired immunity and achievable vaccination coverage (depending on the population age structure), as well as two transmissibility scenarios ((i) with pre-B.1.1.7, and (ii) with exclusively B.1.1.7 variants).

Original language	English
Article number	2100428
Number of pages	7
Journal	Eurosurveillance
Volume	26
Issue number	20
DOIs	https://doi.org/10.2807/1560-7917.ES.2021.26.20.2100428
Publication status	Published - 20 May 2021

Funding

The following funding sources are acknowledged as providing funding for the working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: JYL, KP, YL; INV-016832: SRP; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1139859: BJQ; OPP1183986: ESN; OPP1191821: KO’R). BMGF (INV-016832; OPP1157270: KA). CADDE MR/S0195/1 & FAPESP 18/14389-0: PM. EDCTP2 (RIA2020EF-2983-CSIGN: HPG). Elrha R2HC/UK FCDO/Wellcome Trust/This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care: KvZ. ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (101003688: AG, KP, RCB, WJE, YL). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP, KvZ). This research was partly funded by the Global Challenges Research Fund (GCRF) project ‘RECAP’ managed through RCUK and ESRC (ES/P010873/1: CIJ, TJ). HDR UK (MR/S003975/1: RME). HPRU (NIHR200908: NIB). Innovation Fund (01VSF18015: FK). MRC (MR/N013638/1: EF, NRW; MR/V027956/1: WW). Nakajima Foundation: AE. NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS, YL; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: RME; NIHR200929: CVM, FGS, NGD; PR-OD-1017-20002: AR, WJE). Royal Society (Dorothy Hodgkin Fellowship: RL). Singapore Ministry of Health: RP. UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065 - Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: NGD, RME, SC, TJ, WJE, YL; MR/P014658/1: GMK). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care: TJ. UKRI Research England: NGD. UKRI (MR/V028456/1: YJ). Wellcome Trust (206250/Z/17/Z: TWR; 206471/Z/17/Z: OJB; 208812/Z/17/Z: SC; 210758/Z/18/Z: JDM, JH, SA, SFunk, SRM; 221303/Z/20/Z: MK; UNS110424: FK). No funding: AMF, DCT, YWDC. The following funding sources are acknowledged as providing funding for the working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: MQ; INV-003174: JYL, KP, YL; INV-016832: SRP; NTD Modelling Consortium OPP1184344: CABP, GFM; OPP1139859: BJQ; OPP1183986: ESN; OPP1191821: KO'R). BMGF (INV-016832; OPP1157270: KA). CADDE MR/S0195/1 & FAPESP 18/14389-0: PM. EDCTP2 (RIA2020EF-2983-CSIGN: HPG). Elrha R2HC/UK FCDO/Wellcome Trust/This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care: KvZ. ERC Starting Grant (#757699: MQ). ERC (SG 757688: CJVA, KEA). This project has received funding from the European Union's Horizon 2020 research and innovation programme - project EpiPose (101003688: AG, KP, RCB, WJE, YL). FCDO/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: CABP, KvZ). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: CIJ, TJ). HDR UK (MR/S003975/1: RME). HPRU (NIHR200908: NIB). Innovation Fund (01VSF18015: FK). MRC (MR/N013638/1: EF, NRW; MR/V027956/1: WW). Nakajima Foundation: AE. NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS, YL; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: TJ; NIHR200908: RME; NIHR200929: CVM, FGS, NGD; PR-OD-1017-20002: AR, WJE). Royal Society (Dorothy Hodgkin Fellowship: RL). Singapore Ministry of Health: RP. UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: HPG). UK MRC (MC_PC_19065 - Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: NGD, RME, SC, TJ, WJE, YL; MR/P014658/1: GMK). Authors of this research receive funding from UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care: TJ. UKRI Research England: NGD. UKRI (MR/V028456/1: YJ). Wellcome Trust (206250/Z/17/Z: TWR; 206471/Z/17/Z: OJB; 208812/Z/17/Z: SC; 210758/Z/18/Z: JDM, JH, SA, SFunk, SRM; 221303/Z/20/Z: MK; UNS110424: FK). No funding: AMF, DCT, YWDC.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

herd immunity
covid 19
SARS CoV-2

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Hodgson-etal-Eurosurv-2021-The-potential-for-vaccination-induced-herdFinal published version, 3.81 MBLicence: CC BY 4.0

Cite this

@article{c34d65d54cc643a6bb4fc91466ddef02,

title = "The potential for vaccination-induced herd immunity against the SARS-CoV-2 B.1.1.7 variant",

abstract = "We assess the feasibility of reaching the herd immunity threshold against SARS-CoV-2 through vaccination, considering vaccine effectiveness (VE), transmissibility of the virus and the level of pre-existing immunity in populations, as well as their age structure. If highly transmissible variants of concern become dominant in areas with low levels of naturally-acquired immunity and/or in populations with large proportions of < 15 year-olds, control of infection without non-pharmaceutical interventions may only be possible with a VE ≥ 80\%, and coverage extended to children. Initial reports of vaccine effectiveness against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), the virus responsible for coronavirus disease (COVID-19), have suggested a substantial reduction of the risk of infection [1]. Nevertheless, with the emergence of more transmissible variants such as B.1.1.7 [2], how large-scale immunisation programmes against SARS-CoV-2 will perform is currently unclear. This study assesses the potential of COVID-19 vaccination to generate herd immunity and takes into account vaccine effectiveness, naturally-acquired immunity and achievable vaccination coverage (depending on the population age structure), as well as two transmissibility scenarios ((i) with pre-B.1.1.7, and (ii) with exclusively B.1.1.7 variants).",

keywords = "herd immunity, covid 19, SARS CoV-2",

author = "\{CMMID COVID-19 Working Group\} and David Hodgson and Stefan Flasche and Mark Jit and Kucharski, \{Adam J.\} and Sam Abbott and Edmunds, \{W. John\} and Davies, \{Nicholas G.\} and Eggo, \{Rosalind M.\} and Graham Medley and Jiayao Lei and Yang Liu and Tully, \{Damien C.\} and McCarthy, \{Ciara V.\} and Paul Mee and Akira Endo and Joel Hellewell and Sebastian Funk and Thibaut Jombart and Yalda Jafari and Oliver Brady and Kiesha Prem and Fabienne Krauer and Mihaly Koltai and Waterlow, \{Naomi R.\} and Russell, \{Timothy W.\} and Meakin, \{Sophie R.\} and Kathleen O'Reilly and Bosse, \{Nikos I.\} and William Waites and Nightingale, \{Emily S.\} and Rachel Lowe and Chan, \{Yung Wai Desmond\} and Atkins, \{Katherine E.\} and Quilty, \{Billy J.\} and Sandmann, \{Frank G.\} and \{van Zandvoort\}, Kevin and Villabona-Arenas, \{C. Julian\} and Gibbs, \{Hamish P.\} and Munday, \{James D.\} and Foss, \{Anna M.\} and Amy Gimma and Pearson, \{Carl A.B.\} and Barnard, \{Rosanna C.\} and Matthew Quaife and Sun, \{Fiona Yueqian\} and Alicia Rosello and Rachael Pung and Jarvis, \{Christopher I.\} and Emilie Finch and Kaja Abbas",

year = "2021",

month = may,

day = "20",

doi = "10.2807/1560-7917.ES.2021.26.20.2100428",

language = "English",

volume = "26",

journal = "Eurosurveillance",

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AU - CMMID COVID-19 Working Group

AU - Hodgson, David

AU - Flasche, Stefan

AU - Jit, Mark

AU - Kucharski, Adam J.

AU - Abbott, Sam

AU - Edmunds, W. John

AU - Davies, Nicholas G.

AU - Eggo, Rosalind M.

AU - Medley, Graham

AU - Lei, Jiayao

AU - Liu, Yang

AU - Tully, Damien C.

AU - McCarthy, Ciara V.

AU - Mee, Paul

AU - Endo, Akira

AU - Hellewell, Joel

AU - Funk, Sebastian

AU - Jombart, Thibaut

AU - Jafari, Yalda

AU - Brady, Oliver

AU - Prem, Kiesha

AU - Krauer, Fabienne

AU - Koltai, Mihaly

AU - Waterlow, Naomi R.

AU - Russell, Timothy W.

AU - Meakin, Sophie R.

AU - O'Reilly, Kathleen

AU - Bosse, Nikos I.

AU - Waites, William

AU - Nightingale, Emily S.

AU - Lowe, Rachel

AU - Chan, Yung Wai Desmond

AU - Atkins, Katherine E.

AU - Quilty, Billy J.

AU - Sandmann, Frank G.

AU - van Zandvoort, Kevin

AU - Villabona-Arenas, C. Julian

AU - Gibbs, Hamish P.

AU - Munday, James D.

AU - Foss, Anna M.

AU - Gimma, Amy

AU - Pearson, Carl A.B.

AU - Barnard, Rosanna C.

AU - Quaife, Matthew

AU - Sun, Fiona Yueqian

AU - Rosello, Alicia

AU - Pung, Rachael

AU - Jarvis, Christopher I.

AU - Finch, Emilie

AU - Abbas, Kaja

PY - 2021/5/20

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AB - We assess the feasibility of reaching the herd immunity threshold against SARS-CoV-2 through vaccination, considering vaccine effectiveness (VE), transmissibility of the virus and the level of pre-existing immunity in populations, as well as their age structure. If highly transmissible variants of concern become dominant in areas with low levels of naturally-acquired immunity and/or in populations with large proportions of < 15 year-olds, control of infection without non-pharmaceutical interventions may only be possible with a VE ≥ 80%, and coverage extended to children. Initial reports of vaccine effectiveness against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), the virus responsible for coronavirus disease (COVID-19), have suggested a substantial reduction of the risk of infection [1]. Nevertheless, with the emergence of more transmissible variants such as B.1.1.7 [2], how large-scale immunisation programmes against SARS-CoV-2 will perform is currently unclear. This study assesses the potential of COVID-19 vaccination to generate herd immunity and takes into account vaccine effectiveness, naturally-acquired immunity and achievable vaccination coverage (depending on the population age structure), as well as two transmissibility scenarios ((i) with pre-B.1.1.7, and (ii) with exclusively B.1.1.7 variants).

KW - herd immunity

KW - covid 19

KW - SARS CoV-2

U2 - 10.2807/1560-7917.ES.2021.26.20.2100428

DO - 10.2807/1560-7917.ES.2021.26.20.2100428

M3 - Article

C2 - 34018481

AN - SCOPUS:85106569369

SN - 1025-496X

VL - 26

JO - Eurosurveillance

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IS - 20

M1 - 2100428

ER -

The potential for vaccination-induced herd immunity against the SARS-CoV-2 B.1.1.7 variant

Abstract

Funding

UN SDGs

Keywords

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