Abstract
Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant.
Original language | English |
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Article number | 100482 |
Journal | Patterns |
Volume | 3 |
Issue number | 4 |
Early online date | 9 Mar 2022 |
DOIs | |
Publication status | Published - 8 Apr 2022 |
Keywords
- COVID-19
- DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
- health behaviors
- machine learning
- public goods dilemma
- random forest
- social norms
ASJC Scopus subject areas
- General Decision Sciences
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The biggest pandemic study in the world
Michelle vanDellen & Mioara Cristea
10/07/20
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