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Big data is decision science: The case of COVID-19 vaccination

Author

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  • Jacques Bughin
  • Michele Cincera
  • Dorota Reykowska
  • Rafal Ohme

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, the authors 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, and securing institutional and peer trust are key nudges to convert skeptics to get vaccinated against 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.

Suggested Citation

  • Jacques Bughin & Michele Cincera & Dorota Reykowska & Rafal Ohme, 2021. "Big data is decision science: The case of COVID-19 vaccination," ULB Institutional Repository 2013/342494, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/342494
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    Cited by:

    1. Jacques Bughin & Michele Cincera & Evelyn Kiepfer & Dorota Reykowska & Florian Philippi & Marcin Żyszkiewicz & Rafal Ohme & Dirk Frank, 2023. "Vaccination or NPI? A conjoint analysis of German citizens' preferences in the context of the COVID-19 pandemic," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(1), pages 39-52, February.

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