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. VAN DELLEN, N. Pontus LEANDER, Maximilian AGOSTINI, Tim DRAWS, Andrii GRYGORYSHYN, Ben GÜTZKOW, Jannis KREIENKAMP, Clara S. VETTER, Georgios ABAKOUMKIN, Jamilah Hanum ABDUL KHAIYOM, Vjollca AHMEDI, Handan AKKAS, Carlos A. ALMENARA, Mohsin ATTA, Sabahat Cigdem BAGCI, Sima BASEL, Edona BERISHA KIDA, Allan B.i. BERNARDONicholas 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 VAN BREEN, Kees VAN VEEN, Alexandra VÁZQUEZ, Robin WOLLAST, Victoria Wai-Lan YEUNG, Somayeh ZAND, Iris Lav Žeželj, Bang ZHENG, Andreas ZICK, Claudia ZÚÑIGA, Jocelyn J. BÉLANGER

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Before vaccines for 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-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.
Original languageEnglish
Article number100482
JournalPatterns
Volume3
Issue number4
Early online date9 Mar 2022
DOIs
Publication statusPublished - 8 Apr 2022

Bibliographical note

Funding Information:
The lead author was funded by a NWO Veni Grant (NWO Grant Number VI.Veni.191G.090 ). This research received support from the New York University Abu Dhabi ( VCDSF/75-71015 ), the University of Groningen (Sustainable Society & Ubbo Emmius Fund), and the Instituto de Salud Carlos III ( COV20/00086 ) co-funded by the European Regional Development Fund (ERDF) “A way to make Europe.”

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Machine learning
  • COVID-19
  • Health Behaviors
  • Social Norms
  • Public Goods
  • Dilemma
  • random forest
  • social norms
  • DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
  • machine learning
  • public goods dilemma
  • health behaviors

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