Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patter.2022.100482 | Patterns |
Keywords | DocType | Volume |
machine learning,COVID-19,health behaviors,social norms,public goods dilemma,random forest | Journal | 3 |
Issue | ISSN | Citations |
4 | 2666-3899 | 0 |
PageRank | References | Authors |
0.34 | 0 | 105 |