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

Van Lissa C.J.; Stroebe W.; vanDellen M.R.; Leander N.P.; Agostini M.; Draws T.; Grygoryshyn A.; Gützgow B.; Kreienkamp J.; Vetter C.S.; Abakoumkin G.; Abdul Khaiyom J.H.; Ahmedi V.; Akkas H.; Almenara C.A.; Atta M.; Bagci S.C.; Basel S.; Kida E.B.; Bernardo A.B.I.; Buttrick N.R.; Chobthamkit P.; Choi H.-S.; Cristea M.; Csaba S.; Damnjanović K.; Danyliuk I.; Dash A.; Di Santo D.; Douglas K.M.; Enea V.; Faller D.G.; Fitzsimons G.J.; Gheorghiu A.; Gómez Á.; Hamaidia A.; Han Q.; Helmy M.; Hudiyana J.; Jeronimus B.F.; Jiang D.-Y.; Jovanović V.; Kamenov; Kende A.; Keng S.-L.; Thanh Kieu T.T.; Koc Y.; Kovyazina K.; Kozytska I.; Krause J.; Kruglanksi A.W.; Kurapov A.; Kutlaca M.; Lantos N.A.; Lemay E.P., Jr.; Jaya Lesmana C.B.; Louis W.R.; Lueders A.; Malik N.I.; Martinez A.P.; McCabe K.O.; Mehulić J.; Milla M.N.; Mohammed I.; Molinario E.; Moyano M.; Muhammad H.; Mula S.; Muluk H.; Myroniuk S.; Najafi R.; Nisa C.F.; Nyúl B.; O'Keefe P.A.; Olivas Osuna J.J.; Osin E.N.; Park J.; Pica G.; Pierro A.; Rees J.H.; Reitsema A.M.; Resta E.; Rullo M.; Ryan M.K.; Samekin A.; Santtila P.; Sasin E.M.; Schumpe B.M.; Selim H.A.; Stanton M.V.; Sultana S.; Sutton R.M.; Tseliou E.; Utsugi A.; Anne van Breen J.; Van Veen K.; Vázquez A.; Wollast R.; Wai-Lan Yeung V.; Zand S.; Žeželj I.L.; Zheng B.; Zick A.; Zúñiga C.; Bélanger J.J. · Patterns (2022) · DOI: 10.1016/j.patter.2022.100482

Resumen

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.

Instituciones: Universidad Peruana de Ciencias Aplicadas