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Big Data is Decision Science: the Case of Covid-19 Vaccination

Jacques Bughin, Michele Cincera (), Dorota Reykowska and Rafal Ohme

No 2021-047, iCite Working Papers from ULB -- Universite Libre de Bruxelles

Abstract: Data science has been proven to be an important asset to support better decision-making in a variety of settings, whether it is for a scientist to better predict climate change, for a company to better predict sales, or for a government to anticipate voting preferences. In this research, we leverage Random Forest (RF) as one of the most effective machine learning techniques using big data to predict vaccine intent in five European countries. The findings support the idea that outside of vaccine features, building adequate perception of the risk of contamination, as well securing institutional and peer trust are key nudges to convert skeptics to get vaccinated against the covid-19. What machine learning techniques further add beyond traditional regression techniques, is some extra granularity in factors affecting vaccine preferences (twice more factors than logistic regression). Other factors that emerge as predictors of vaccine intent are compliance appetite with non-pharmaceutical protective measures, as well as perception of the crisis duration.

Keywords: Attitudes; Big data; Covid-19; iCode™; Machine learning techniques; Random Forest; Response time; Vaccination (search for similar items in EconPapers)
Pages: 21 p.
Date: 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp and nep-env
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Working Paper: Big data is decision science: The case of COVID-19 vaccination (2021)
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