Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.
Patterns (N Y)
; 3(4): 100482, 2022 Apr 08.
Article
in English
| MEDLINE | ID: covidwho-1730022
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Language:
English
Journal:
Patterns (N Y)
Year:
2022
Document Type:
Article
Affiliation country:
J.patter.2022.100482
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