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

• Main Authors: Van Lissa, Caspar J, Stroebe, Wolfgang, vanDellen, Michelle R, Leander, N Pontus, Agostini, Maximilian, Draws, Tim, Grygoryshyn, Andrii, Gutzkow, Ben, Kreienkamp, Jannis, Vetter, Clara S., Abakoumkin, Georgios, Abdul Khaiyom, Jamilah Hanum, Ahmedi, Vjollca, Akkas, Handan, Almenara, Carlos A, Atta, Mohsin, Bagci, Sabahat Cigdem, Basel, Sima, Kida, Edona Berisha, Bernado, Allan B. I., Buttrick, Nicholas R, Chobthamkit, Phatthanakit, Choi, Hoon-Seok, Cristea, Mioara, Csaba, Sara, Damnjanović, Kaja, Danyliuk, Ivan, Dash, Arobindu, Di Santo, Daniela, Douglas, Karen M, Enea, Violeta, Faller, Daiane, Fitzsimons, Gavan J, Gheorghiu, Alexandra, Gómez, Ángel, Hamaidia, Ali, Han, Qing, Helmy, Mai, Hudiyana, Joevarian, Jeronimus, Bertus F, Jiang, Ding-Yu, Jovanović, Veljko, Kamenov, Zeljka, Kende, Anna, Keng, Shian-Ling, Tra, Thi Thanh Kieu, Koc, Yasin, Kovyazina, Kamila, Kozytska, Inna, Krause, Joshua, Kruglanski, Arie W, Kurapov, Anton, Kutlaca, Maja, Lantos, Nóra Anna, Lemay Jr., Edward P, Lesmana, Cokorda Bagus J, Louis, Winnifred R, Lueders, Adrian, Iqbal Malik, Najma, Martinez, Anton P, McCabe, Kira O, Mehulić, Jasmina, Milla, Mirra Noor, Mohammed, Idris, Molinario, Erica, Moyano, Manuel, Muhammad, Hayat, Mula, Silvana, Muluk, Hamdi, Myroniuk, Solomiia, Najafi, Reza, Nisa, Claudia F, Nyúl, Boglárka, O'Keefe, Paul A, Osuna, Jose Javier Olivas, Osin, Evgeny N, Park, Joonha, Pica, Gennaro, Pierro, Antonio, Rees, Jonas H, Reitsema, Anne Margit, Resta, Elena, Rullo, Marika, Ryan, Michelle K, Samekin, Adil, Santilla, Pekka, Sasin, Edyta, Schumpe, Birga M, Selim, Heyla A, Stanton, Michael Vicente, Sultana, Samiah, Sutton, Robbie M, Tseliou, Eleftheria, Utsugi, Akira, van Breen, Jolien A, Van Veen, Kees, Vázquez, Alexandra, Wollast, Robin, Yeung, Victoria Wai-lan, Zand, Somayeh, Žeželj, Iris Lav, Zheng, Bang, Zick, Andreas, Zúñiga, Claudia, Belanger, Jocelyn J,
• Format: Article
• Language: English
• Published: 2022
• Subjects: BF Psychology; BF636 Applied psychology; Q Science (General); QA Mathematics; QA75 Electronic computers. Computer science; QA76 Computer software; RA Public aspects of medicine; RA0421 Public health. Hygiene. Preventive Medicine; RA643 Communicable Diseases and Public Health; RA644.C67 Coronavirus infections. COVID-19 (Disease). COVID-19 Pandemic, 2020; T Technology (General); T10.5 Communication of technical information; T351 Mechanical drawing. Engineering graphics
• Online Access: http://irep.iium.edu.my/97637/1/2022_PsyCorona%20Collaboration_Patterns.pdf; http://irep.iium.edu.my/97637/
• Institution: Universiti Islam Antarabangsa Malaysia

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 individuallevel 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.