Optimising health and economic impacts of COVID-19 vaccine prioritisation strategies in the WHO European Region: a mathematical modelling study

Yang Liu*, Frank G. Sandmann, Rosanna C. Barnard, Carl A.B. Pearson, Roberta Pastore, Richard Pebody, Stefan Flasche, Mark Jit

*Corresponding author for this work

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Background: Countries in the World Health Organization (WHO) European Region differ in terms of the COVID-19 vaccine supply conditions. We evaluated the health and economic impact of different age-based vaccine prioritisation strategies across this demographically and socio-economically diverse region. 

Methods: We fitted age-specific compartmental models to the reported daily COVID-19 mortality in 2020 to inform the immunity level before vaccine roll-out. Models capture country-specific differences in population structures, contact patterns, epidemic history, life expectancy, and GDP per capita. We examined four strategies that prioritise: all adults (V+), younger (20-59 year-olds) followed by older adults (60+) (V20), older followed by younger adults (V60), and the oldest adults (75+) (V75) followed by incrementally younger age groups. We explored four roll-out scenarios (R1-4) — the slowest scenario (R1) reached 30% coverage by December 2022 and the fastest (R4) 80% by December 2021. Five decision-making metrics were summarised over 2021-22: mortality, morbidity, and losses in comorbidity-adjusted life expectancy, comorbidity- and quality-adjusted life years, and human capital. Six vaccine profiles were tested — the highest performing vaccine has 95% efficacy against both infection and disease, and the lowest 50% against diseases and 0% against infection. 

Findings: Of the 20 decision-making metrics and roll-out scenario combinations, the same optimal strategy applied to all countries in only one combination; V60 was more or similarly desirable than V75 in 19 combinations. Of the 38 countries with fitted models, 11-37 countries had variable optimal strategies by decision-making metrics or roll-out scenarios. There are greater benefits in prioritising older adults when roll-out is slow and when vaccine profiles are less favourable. 

Interpretation: The optimal age-based vaccine prioritisation strategies were sensitive to country characteristics, decision-making metrics, and roll-out speeds. A prioritisation strategy involving more age-based stages (V75) does not necessarily lead to better health and economic outcomes than targeting broad age groups (V60). Countries expecting a slow vaccine roll-out may particularly benefit from prioritising older adults. 

Funding: World Health Organization, Bill and Melinda Gates Foundation, the Medical Research Council (United Kingdom), the National Institute of Health Research (United Kingdom), the European Commission, the Foreign, Commonwealth and Development Office (United Kingdom), Wellcome Trust

Original languageEnglish
Article number100267
JournalThe Lancet Regional Health - Europe
Early online date30 Nov 2021
Publication statusPublished - Jan 2022

Bibliographical note

Funding Information: We graciously thank the following agencies for their support to this work: World Health Organization (202604060), Bill & Melinda Gates Foundation (INV-003174, OPP1184344), European Commission (101003688), Medical Research Council (MC_PC_19065), National Institute of Health Research (200929), Foreign, Commonwealth and Development Office (UK)/ Wellcome Trust (221303/Z/20/Z), Wellcome Trust (208812/Z/17/Z). FGS and MJ were supported by the NIHR Health Protection Research Unit (HPRU) in Modelling and Health Economics, a partnership between Public Health England (PHE), Imperial College London, and the London School of Hygiene & Tropical Medicine (LSHTM; grant code NIHR200908). We are grateful for the support, comments, and feedback from the members and organisers of WHO's Regional Working Group on COVID-19 vaccination and development in the European Region, Focus Group 2 “Immunization Strategy and decision making.” We thank Dr Nicholas G Davies for his valuable feedback and work on CovidM — much of what this analysis has been built upon. We thank the peer reviewers for their time and thoughtful feedback.

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: KP; INV-016832: SRP; NTD Modelling Consortium OPP1184344: GFM; OPP1139859: BJQ; OPP1191821: KO'R). BMGF (INV-016832; OPP1157270: KA). CADDE MR/S0195/1 & FAPESP 18/14389-0 (PM). EDCTP2 (RIA2020EF-2983-CSIGN: HPG). 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, KLM, KP, WJE). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: CIJ). HDR UK (MR/S003975/1: RME). HPRU (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 Care200908: NIB). MRC (MR/N013638/1: EF; MR/V027956/1: WW). Nakajima Foundation (AE). NIHR (16/136/46: BJQ; 16/137/109: BJQ, FYS; 1R01AI141534-01A1: DH; NIHR200908: AJK, LACC, RME; NIHR200929: CVM, 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, WJE; MR/P014658/1: GMK). UKRI (MR/V028456/1: YJ). Wellcome Trust (206250/Z/17/Z: AJK, 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). No funding (DCT, SH).

Open Access: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Publisher Copyright: © 2021 The Author(s). Published by Elsevier Ltd.

Citation: Yang Liu, Frank G. Sandmann, Rosanna C. Barnard, Carl A.B. Pearson, Roberta Pastore, Richard Pebody, Stefan Flasche, Mark Jit, Optimising health and economic impacts of COVID-19 vaccine prioritisation strategies in the WHO European Region: a mathematical modelling study, The Lancet Regional Health - Europe, Volume 12, 2022, 100267, ISSN 2666-7762.

DOI: https://doi.org/10.1016/j.lanepe.2021.100267.


  • COVID-19
  • Europe
  • Health economics
  • Mathematical modelling
  • Multicountry analysis
  • Policy evaluation
  • Vaccine policy


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