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

SUBMITTER: Van Lissa CJ 

PROVIDER: S-EPMC8904175 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature