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The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Author

Listed:
  • Stephane Hess

    (University of Leeds)

  • Emily Lancsar

    (ANU - Australian National University)

  • Petr Mariel

    (UPV / EHU - University of the Basque Country = Euskal Herriko Unibertsitatea)

  • Jürgen Meyerhoff

    (TU - Technical University of Berlin / Technische Universität Berlin)

  • Fangqing Song

    (University of Leeds, UCL - University College of London [London])

  • Eline van den Broek-Altenburg

    (University of Vermont [Burlington])

  • Olufunke Alaba

    (University of Cape Town)

  • Gloria Amaris

    (University of Leeds, NTNU - Norwegian University of Science and Technology [Trondheim] - NTNU - Norwegian University of Science and Technology)

  • Julián Arellana

    (Universidad del Norte, Barranquilla)

  • Leonardo Basso

    (UCHILE - Universidad de Chile = University of Chile [Santiago])

  • Jamie Benson

    (University of Vermont [Burlington])

  • Luis Bravo-Moncayo

    (UDLA - Universidad de Las Américas [Ecuador], UTN - Universidad Técnica del Norte)

  • Olivier Chanel

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Syngjoo Choi

    (SNU - Seoul National University [Seoul])

  • Romain Crastes Dit Sourd

    (University of Leeds)

  • Helena Bettella Cybis

    (UFRGS - Universidade Federal do Rio Grande do Sul [Porto Alegre])

  • Zack Dorner

    (University of Waikato [Hamilton])

  • Paolo Falco

    (ITU - IT University of Copenhagen)

  • Luis Garzón-Pérez

    (UTN - Universidad Técnica del Norte)

  • Kathryn Glass

    (ANU - Australian National University)

  • Luis Guzman

    (UNIANDES - Universidad de los Andes [Bogota])

  • Zhiran Huang

    (HKU - The University of Hong Kong)

  • Elisabeth Huynh

    (ANU - Australian National University)

  • Bongseop Kim

    (SNU - Seoul National University [Seoul])

  • Abisai Konstantinus

    (Ndatara surveys)

  • Iyaloo Konstantinus

    (Namibia Institute of Pathology)

  • Ana Margarita Larranaga

    (UFRGS - Universidade Federal do Rio Grande do Sul [Porto Alegre])

  • Alberto Longo

    (QUB - Queen's University [Belfast])

  • Becky P.Y. Loo

    (HKU - The University of Hong Kong)

  • Malte Oehlmann

    (TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich)

  • Vikki O'Neill

    (QUB - Queen's University [Belfast])

  • Juan de Dios Ortúzar

    (UC - Pontificia Universidad Católica de Chile)

  • María José Sanz

    (BC3 - Basque Centre for Climate Change, Ikerbasque - Basque Foundation for Science)

  • Olga Sarmiento

    (UNIANDES - Universidad de los Andes [Bogota])

  • Hazvinei Tamuka Moyo

    (University of Cape Town)

  • Steven Tucker

    (University of Waikato [Hamilton])

  • Yacan Wang

    (BJTU - Beijing Jiaotong University)

  • Yu Wang

    (BJTU - Beijing Jiaotong University)

  • Edward J.D. Webb

    (University of Leeds)

  • Junyi Zhang

    (Hiroshima University)

  • Mark H.P. Zuidgeest

    (University of Cape Town)

Abstract

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

Suggested Citation

  • Stephane Hess & Emily Lancsar & Petr Mariel & Jürgen Meyerhoff & Fangqing Song & Eline van den Broek-Altenburg & Olufunke Alaba & Gloria Amaris & Julián Arellana & Leonardo Basso & Jamie Benson & Luis, 2022. "The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents," Post-Print hal-03778395, HAL.
  • Handle: RePEc:hal:journl:hal-03778395
    DOI: 10.1016/j.socscimed.2022.114800
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-03778395
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    References listed on IDEAS

    as
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    1. Haghani, Milad & Bliemer, Michiel C.J. & de Bekker-Grob, Esther W., 2022. "Applications of discrete choice experiments in COVID-19 research: Disparity in survey qualities between health and transport fields," Journal of choice modelling, Elsevier, vol. 44(C).

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