Make it or break it: On-time vaccination intent at the time of Covid-19

被引:3
作者
Bughin, Jacques [1 ,2 ,3 ]
Cincera, Michele [1 ,7 ]
Peters, Kelly [4 ]
Reykowska, Dorota [5 ]
Zyszkiewicz, Marcin [5 ]
Ohme, Rafal [5 ,6 ]
机构
[1] Univ libre Bruxelles, Solvay Brussels Sch Econ & Management, iCite & ECARES, Brussels, Belgium
[2] MachaonAdvisory, FortinoCapital, Antler, Amsterdam, Netherlands
[3] Portulans Inst, Washington, DC USA
[4] BEworks, Wroclaw, Poland
[5] Neurohm, Wroclaw, Poland
[6] WSB Univ, Neurohm, Torun, Poland
[7] Univ libre Bruxelles, Solvay Brussels Sch Econ & Management, ICITE CP146, 50, Ave FD Roosevelt, B-1050 Brussels, Belgium
关键词
Covid-19; Vaccine strategy; Machine Learning; Random-forest; Social trust; VACCINES; ATTITUDE; HEALTH;
D O I
10.1016/j.vaccine.2023.02.014
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
On-time effective vaccination is critical to curbing a pandemic, but this is often hampered by citizens' hesitancy to get quickly vaccinated. This research concentrates on the hypothesis that, besides traditional factors in the literature, vaccination success would hinge on two dimensions: a) addressing a broader set of risk perception factors than health-related issues only, and b) securing sufficient social and institu-tional trust at the time of vaccination campaign launch. We test this hypothesis regarding Covid-19 vac-cination preferences in six European countries and at the early stage of the pandemic by April 2020. We find that addressing the two roadblock dimensions could further boost Covid-19 vaccination coverage by 22%. The study also offers three extra innovations. The first is that the traditional segmentation logic between vaccine "acceptors", "hesitants" and "refusers" is further justified by the fact that segments have different attitudes: refusers care less about health issues than they are worried about family tensions and finance (dimension 1 of our hypothesis). In contrast, hesitants are the battlefield for more transparency by media and government actions (dimension 2 of our hypothesis). The second added value is that we extend our hypothesis testing with a supervised non-parametric machine learning technique (Random Forests). Again, consistent with our hypothesis, this method picks up higher-order interaction between risk and trust variables that strongly predict on-time vaccination intent. We finally explicitly adjust survey responses to account for possible reporting bias. Among others, vaccine-reluctant citizens may under-report their limited will to get vaccinated.(c) 2023 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2063 / 2072
页数:10
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