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

Publication ,  Journal Article
Van Lissa, CJ; Stroebe, W; vanDellen, MR; Leander, NP; Agostini, M; Draws, T; Grygoryshyn, A; Gützgow, B; Kreienkamp, J; Vetter, CS; Ahmedi, V ...
Published in: Patterns (New York, N.Y.)
April 2022
Published version (DOI)

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.

Duke Scholars

Claudia Fernandes Nisa
Author Claudia Fernandes Nisa DKU Faculty
Gavan J. Fitzsimons
Author Gavan J. Fitzsimons Fuqua School of Business
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Published In

Patterns (New York, N.Y.)

DOI

10.1016/j.patter.2022.100482

EISSN

2666-3899

ISSN

2666-3899

Publication Date

April 2022

Volume

3

Issue

4

Start / End Page

100482

Related Subject Headings

  • 4905 Statistics
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Van Lissa, C. J., Stroebe, W., vanDellen, M. R., Leander, N. P., Agostini, M., Draws, T., … Bélanger, J. J. (2022). Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns (New York, N.Y.), 3(4), 100482. https://doi.org/10.1016/j.patter.2022.100482
Van Lissa, Caspar J., Wolfgang Stroebe, Michelle R. vanDellen, N Pontus Leander, Maximilian Agostini, Tim Draws, Andrii Grygoryshyn, et al. “Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.” Patterns (New York, N.Y.) 3, no. 4 (April 2022): 100482. https://doi.org/10.1016/j.patter.2022.100482.
Van Lissa CJ, Stroebe W, vanDellen MR, Leander NP, Agostini M, Draws T, et al. Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns (New York, NY). 2022 Apr;3(4):100482.
Van Lissa, Caspar J., et al. “Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.” Patterns (New York, N.Y.), vol. 3, no. 4, Apr. 2022, p. 100482. Epmc, doi:10.1016/j.patter.2022.100482.
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. Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns (New York, NY). 2022 Apr;3(4):100482.

Published In

Patterns (New York, N.Y.)

DOI

10.1016/j.patter.2022.100482

EISSN

2666-3899

ISSN

2666-3899

Publication Date

April 2022

Volume

3

Issue

4

Start / End Page

100482

Related Subject Headings

  • 4905 Statistics
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation