Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
- PMID: 35282654
- PMCID: PMC8904175
- DOI: 10.1016/j.patter.2022.100482
Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
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.
Keywords: COVID-19; health behaviors; machine learning; public goods dilemma; random forest; social norms.
© 2022 The Author(s).
Conflict of interest statement
The authors declare no competing interests.
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References
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- Omeife H.O. Coronavirus: distancing and handwashing could lower flu rates, too. MedicalXpress. 2020 https://medicalxpress.com/news/2020-04-coronavirus-distancing-handwashin...
-
- Van Lissa C.J. CRAN; 2018. Metaforest: Exploring Heterogeneity in Meta-Analysis Using Random Forests (0.1.2) [R-Package] https://cran.r-project.org/package=metaforest.
-
- Hastie T., Tibshirani R., Friedman J. Springer; 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
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