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

Caspar J Van Lissa; Wolfgang Stroebe; Michelle R vanDellen; N Pontus Leander; Maximilian Agostini; Tim Draws; Andrii Grygoryshyn; Ben Gützgow; Jannis Kreienkamp; Clara S Vetter; +95 more... Georgios Abakoumkin; Jamilah Hanum Abdul Khaiyom; Vjolica Ahmedi; Handan Akkas; Carlos A Almenara; Mohsin Atta; Sabahat Cigdem Bagci; Sima Basel; Edona Berisha Kida; Allan BI Bernardo; Nicholas R Buttrick; Phatthanakit Chobthamkit; Hoon-Seok Choi; Mioara Cristea; Sára Csaba; Kaja Damnjanović; Ivan Danyliuk; Arobindu Dash; Daniela Di Santo; Karen M Douglas; Violeta Enea; Daiane Gracieli Faller; Gavan J Fitzsimons; Alexandra Gheorghiu; Ángel Gómez; Ali Hamaidia; Qing Han; Mai Helmy; Joevarian Hudiyana; Bertus F Jeronimus; Ding-Yu Jiang; Veljko Jovanović; Željka Kamenov; Anna Kende; Shian-Ling Keng; Tra Thi Thanh Kieu; Yasin Koc; Kamila Kovyazina; Inna Kozytska; Joshua Krause; Arie W Kruglanksi; Anton Kurapov; Maja Kutlaca; Nóra Anna Lantos; Edward P Lemay; Cokorda Bagus Jaya Lesmana; Winnifred R Louis; Adrian Lueders; Najma Iqbal Malik; Anton P Martinez; Kira O McCabe; Jasmina Mehulić; Mirra Noor Milla; Idris Mohammed; Erica Molinario; Manuel Moyano; Hayat Muhammad; Silvana Mula; Hamdi Muluk; Solomiia Myroniuk; Reza Najafi; Claudia F Nisa; Boglárka Nyúl; Paul A O'Keefe; Jose Javier Olivas Osuna; Evgeny N Osin; Joonha Park; Gennaro Pica; Antonio Pierro; Jonas H Rees; Anne Margit Reitsema; Elena Resta; Marika Rullo; Michelle K Ryan; Adil Samekin; Pekka Santtila; Edyta M Sasin; Birga M Schumpe; Heyla A Selim; Michael Vicente Stanton; Samiah Sultana; Robbie M Sutton; Eleftheria Tseliou; Akira Utsugi; Jolien Anne van Breen; Kees Van Veen; Alexandra Vázquez; Robin Wollast; Victoria Wai-Lan Yeung; Somayeh Zand; Iris Lav Žeželj; Bang Zheng ORCID logo; Andreas Zick; Claudia Zúñiga; Jocelyn J Bélanger; (2022) Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns, 3 (4). 100482-. ISSN 2666-3899 DOI: 10.1016/j.patter.2022.100482
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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.


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