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The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents
- Publication Year :
- 2022
-
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.
Details
- Database :
- OAIster
- Notes :
- We acknowledge financial support as follows: CN Chinese National Natural Science Foundation (72071017), the joint project of the National Natural Science Foundation of China and the Joint Programming Initiative Urban Europe (NSFC – JPI UE) (‘U- PASS’, 71961137005). CL Instituto Sistemas Complejos de Ingeniería (ISCI), through grant ANID PIA/BASAL AFB180003. ES FEDER/Ministry of Science, Innovation and Universities through grant PID2020-113650RB-I00; Basque Government through grant IT1359-19 (UPV/EHU Econometrics Research Group); BERC 2018–2021 programme; MICINN María de Maeztu excellence accreditation (MDM- 2017-0714). FR FR French National Research Agency Grants ANR-17-EURE-0020 and the Excellence Initiative of Aix-Marseille University - A*MIDEX. KR Creative-Pioneering Researchers Program through Seoul Na- tional University. NA Ndatara Surveys. NZ Waikato Management School. UK European Research Council through the consolidator grant 615596-DECISIONS; internal funding through the Choice Modelling Centre (CMC). We would also like to express our thanks to technical support from Aix-Marseille University, and thank Sofia Hern ́andez Benavides (CL), Simon Dec Pedersen (DK) and Robbie Maris (NZ) for help in data preparation and analysis., English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1346976877
- Document Type :
- Electronic Resource