The path towards herd immunity

Predicting Covid-19 vaccination uptake through results from a stated choice study across six continents

Bibliographic Data

ID4595137
AuthorsStephane Hess (0000-0002-3650-2518, University of Leeds), Emily Lancsar (0000-0003-2404-6735, Australian National University), Petr Mariel (0000-0002-7412-0684, University of the Basque Country), Jürgen Meyerhoff (0000-0003-4333-8514, Technische Universität Berlin), Fengxiang Song (0000-0001-7910-4635, University of Leeds), Fangqing Song, Eline Van Den Broek-Altenburg (0000-0002-4831-9083, University of Vermont), Olufunke A Alaba, Olufunke Alaba (0000-0002-2404-0977, University of Cape Town), Gloria Amaris (0000-0002-6577-7852, University of Leeds), Julián Arellana (0000-0001-7834-5541, Universidad del Norte), Leonardo J Basso (0000-0001-9097-6266, University of Chile), Jamie Benson (0000-0002-0709-4711, University of Vermont), Luis Bravo-Moncayo (0000-0003-4495-2073, Universidad Técnica del Norte), Olivier Chanel (0000-0002-8221-7558), Syngjoo Choi (0000-0003-4006-0377, Seoul National University), Romain Crastes Dit Sourd (0000-0003-4506-2910, University of Leeds), Helena Bettella Cybis (Universidade Federal do Rio Grande do Sul), Zack Dorner (0000-0003-4216-6714, University of Waikato), Paolo Falco (0000-0002-0270-172X, IT University of Copenhagen), Luis Garzón-Pérez (Universidad Técnica del Norte), Kenneth Gla (0000-0001-5905-1310, Australian National University), Kathryn Gla, L A Guzman (0000-0002-6487-7579, Universidad de los Andes), Zhiran Huang (0000-0001-9567-8399, University of Hong Kong), Elisabeth Huynh (0000-0002-1855-3143, Australian National University), Bongseop Kim (Seoul National University), Abisai Konstantinus (0000-0002-4363-8691), Iyaloo Konstantinus, Iyaloo N Konstantinus (Namibia Institute of Pathology), Ana Margarita Larranaga (0000-0001-7504-2369, Universidade Federal do Rio Grande do Sul), Alberto Longo (0000-0001-8373-4912, Queen's University Belfast), Becky P Y Loo (0000-0003-0822-5354, University of Hong Kong), Malte Oehlmann (Technical University of Munich), Victoria O’neill (0000-0003-2252-5759, Queen's University Belfast), Vikki O''Neill, Vikki O'Neill, Juan De Dios Ortúzar (0000-0003-3452-3574, Pontificia Universidad Católica de Chile), María José Sanz (0000-0003-0471-3094, Basque Centre for Climate Change), Olga L Sarmiento (0000-0002-9190-3568, Universidad de los Andes), Hazvinei Tamuka Moyo (University of Cape Town), Steven Tucker (0000-0002-5273-8096, University of Waikato), Yacan Wang (0000-0001-8908-6338, Beijing Jiaotong University), Yu Wang (0009-0005-4222-2626, Beijing Jiaotong University), Edward J D Webb (0000-0001-7918-839X, University of Leeds), Junyi Zhang (0000-0002-3267-542X, Hiroshima University), Mark Zuidgeest (0000-0002-0640-006X, University of Cape Town), Mark H P Zuidgeest
Year2022
Volume298
Pages114800
Publication date2022-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Science & Medicine (JOURNAL)
Journal identifiersISSN: 0277-9536 • E-ISSN: 1873-5347
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.socscimed.2022.114800
PMID35287066
OpenAlexW4213075636
LanguageEN
Citations received6
References cited52

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

2019-20 coronavirus outbreak · Biology · Coronavirus disease 2019 (COVID-19 · Disease · Geography · Herd immunity · Immune system · Immunity · Infectious disease (medical specialty · Outbreak · Pandemic · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Sociology · Vaccination · COVID-19 epidemiological studies · Demography · Medicine · SARS-CoV-2 and COVID-19 Research · Vaccine Coverage and Hesitancy · Immunology · Internal Medicine · Virology

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