Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

In: /www.sciencedirect.com

Van Lissa, C., Stroebe, W., vanDellen, M. R., Leander, N. P., (…) & Belanger, J. J. (2022). Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns, 3, 100384, https://doi.org/10.1016/j.patter.2022.100482

Highlights

• We studied predictors of COVID-19 prevention behaviors in a cross-national study

• The strongest predictors related to injunctive norms

Summary

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.

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