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 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.

Metadata

Item Type:	Article
Authors/Creators:	Van Lissa, CJ Stroebe, W vanDellen, MR Leander, NP Agostini, M Draws, T Grygoryshyn, A Gützgow, B Kreienkamp, J Vetter, CS Abakoumkin, G Abdul Khaiyom, JH Ahmedi, V Akkas, H Almenara, CA Atta, M Bagci, SC Basel, S Kida, EB Bernardo, ABI Buttrick, NR Chobthamkit, P Choi, H-S Cristea, M Csaba, S Damnjanović, K Danyliuk, I Dash, A Di Santo, D Douglas, KM Enea, V Faller, DG Fitzsimons, GJ Gheorghiu, A Gómez, Á Hamaidia, A Han, Q Helmy, M Hudiyana, J Jeronimus, BF Jiang, D-Y Jovanović, V Kamenov, Ž Kende, A Keng, S-L Thanh Kieu, TT Koc, Y Kovyazina, K Kozytska, I Krause, J Kruglanksi, AW Kurapov, A Kutlaca, M Lantos, NA Lemay, EP Jaya Lesmana, CB Louis, WR Lueders, A Malik, NI Martinez, AP McCabe, KO Mehulić, J Milla, MN Mohammed, I Molinario, E Moyano, M Muhammad, H Mula, S Muluk, H Myroniuk, S Najafi, R Nisa, CF Nyúl, B O’Keefe, PA Olivas Osuna, JJ Osin, EN Park, J Pica, G Pierro, A Rees, JH Reitsema, AM Resta, E Rullo, M Ryan, MK Samekin, A Santtila, P Sasin, EM Schumpe, BM Selim, HA Stanton, MV Sultana, S Sutton, RM Tseliou, E Utsugi, A Anne van Breen, J Van Veen, K Vázquez, A Wollast, R Wai-Lan Yeung, V Zand, S Žeželj, IL Zheng, B Zick, A Zúñiga, C Bélanger, JJ
Copyright, Publisher and Additional Information:	© 2022 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:	machine learning; COVID-19; health behaviors; social norms; public goods dilemma; random forest
Dates:	Published: 8 April 2022 Published (online): 9 March 2022 Accepted: 4 March 2022
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	21 Apr 2022 15:42
Last Modified:	21 Apr 2022 15:42
Status:	Published
Publisher:	Elsevier BV
Refereed:	Yes
Identification Number:	10.1016/j.patter.2022.100482
Related URLs:	Dataset
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:185813

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

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

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