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

PsyCorona Collaboration; 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; Georgios Abakoumkin; Jamilah Hanum Abdul Khaiyom; Vjolica Ahmedi; Handan Akkas; Carlos A. Almenara; Mohsin Atta; Sabahat Cigdem Bagci; Sima Basel; Edona Berisha Kida; Allan B.I. 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; Bertus F. Jeronimus; Yasin Koc; Joshua Krause; Maja Kutlaca; Anton P. Martinez; Kira O. McCabe; Solomiia Myroniuk; Boglárka Nyúl; Anne Margit Reitsema; Michelle K. Ryan; Edyta M. Sasin; Samiah Sultana; Kees Van Veen

doi:10.1016/j.patter.2022.100482

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

PsyCorona Collaboration, 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, Georgios Abakoumkin, Jamilah Hanum Abdul Khaiyom, Vjolica Ahmedi, Handan Akkas, Carlos A. Almenara, Mohsin Atta, Sabahat Cigdem Bagci, Sima Basel, Edona Berisha KidaAllan B.I. 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, Bertus F. Jeronimus, Yasin Koc, Joshua Krause, Maja Kutlaca, Anton P. Martinez, Kira O. McCabe, Solomiia Myroniuk, Boglárka Nyúl, Anne Margit Reitsema, Michelle K. Ryan, Edyta M. Sasin, Samiah Sultana, Kees Van Veen

^*Corresponding author for this work

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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.

Original language	English
Article number	100482
Number of pages	14
Journal	Patterns (New York, N.Y.)
Volume	3
Issue number	4
Early online date	2022
DOIs	https://doi.org/10.1016/j.patter.2022.100482
Publication status	Published - 8-Apr-2022

Keywords

COVID-19
DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
health behaviors
machine learning
public goods dilemma
random forest
social norms

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PsyCorona Collaboration, Van Lissa, C. J., Stroebe, W., vanDellen, M. R., Leander, N. P., Agostini, M., Draws, T., Grygoryshyn, A., Gützgow, B., Kreienkamp, J., Vetter, C. S., Abakoumkin, G., Abdul Khaiyom, J. H., Ahmedi, V., Akkas, H., Almenara, C. A., Atta, M., Bagci, S. C., Basel, S., ... Van Veen, K. (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), Article 100482. https://doi.org/10.1016/j.patter.2022.100482

@article{5988259b65494c54873f9adce74f4b93,

title = "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.",

keywords = "COVID-19, DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem, health behaviors, machine learning, public goods dilemma, random forest, social norms",

author = "{PsyCorona Collaboration} and {Van Lissa}, {Caspar J.} and Wolfgang Stroebe and vanDellen, {Michelle R.} and Leander, {N. Pontus} and Maximilian Agostini and Tim Draws and Andrii Grygoryshyn and Ben G{\"u}tzgow and Jannis Kreienkamp and Vetter, {Clara S.} and Georgios Abakoumkin and {Abdul Khaiyom}, {Jamilah Hanum} and Vjolica Ahmedi and Handan Akkas and Almenara, {Carlos A.} and Mohsin Atta and Bagci, {Sabahat Cigdem} and Sima Basel and Kida, {Edona Berisha} and Bernardo, {Allan B.I.} and Buttrick, {Nicholas R.} and Phatthanakit Chobthamkit and Choi, {Hoon Seok} and Mioara Cristea and S{\'a}ra Csaba and Kaja Damnjanovi{\'c} and Ivan Danyliuk and Arobindu Dash and {Di Santo}, Daniela and Douglas, {Karen M.} and Violeta Enea and Faller, {Daiane Gracieli} and Fitzsimons, {Gavan J.} and Alexandra Gheorghiu and {\'A}ngel G{\'o}mez and Ali Hamaidia and Qing Han and Jeronimus, {Bertus F.} and Yasin Koc and Joshua Krause and Maja Kutlaca and Martinez, {Anton P.} and McCabe, {Kira O.} and Solomiia Myroniuk and Bogl{\'a}rka Ny{\'u}l and Reitsema, {Anne Margit} and Ryan, {Michelle K.} and Sasin, {Edyta M.} and Samiah Sultana and {Van Veen}, Kees",

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: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = apr,

day = "8",

doi = "10.1016/j.patter.2022.100482",

language = "English",

volume = "3",

journal = "Patterns (New York, N.Y.)",

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PsyCorona Collaboration, 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, HS, 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, Jeronimus, BF , Koc, Y , Krause, J, Kutlaca, M, Martinez, AP, McCabe, KO, Myroniuk, S, Nyúl, B, Reitsema, AM, Ryan, MK, Sasin, EM, Sultana, S & Van Veen, K 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.), vol. 3, no. 4, 100482. https://doi.org/10.1016/j.patter.2022.100482

TY - JOUR

T1 - Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

AU - PsyCorona Collaboration

AU - Van Lissa, Caspar J.

AU - Stroebe, Wolfgang

AU - vanDellen, Michelle R.

AU - Leander, N. Pontus

AU - Agostini, Maximilian

AU - Draws, Tim

AU - Grygoryshyn, Andrii

AU - Gützgow, Ben

AU - Kreienkamp, Jannis

AU - Vetter, Clara S.

AU - Abakoumkin, Georgios

AU - Abdul Khaiyom, Jamilah Hanum

AU - Ahmedi, Vjolica

AU - Akkas, Handan

AU - Almenara, Carlos A.

AU - Atta, Mohsin

AU - Bagci, Sabahat Cigdem

AU - Basel, Sima

AU - Kida, Edona Berisha

AU - Bernardo, Allan B.I.

AU - Buttrick, Nicholas R.

AU - Chobthamkit, Phatthanakit

AU - Choi, Hoon Seok

AU - Cristea, Mioara

AU - Csaba, Sára

AU - Damnjanović, Kaja

AU - Danyliuk, Ivan

AU - Dash, Arobindu

AU - Di Santo, Daniela

AU - Douglas, Karen M.

AU - Enea, Violeta

AU - Faller, Daiane Gracieli

AU - Fitzsimons, Gavan J.

AU - Gheorghiu, Alexandra

AU - Gómez, Ángel

AU - Hamaidia, Ali

AU - Han, Qing

AU - Jeronimus, Bertus F.

AU - Koc, Yasin

AU - Krause, Joshua

AU - Kutlaca, Maja

AU - Martinez, Anton P.

AU - McCabe, Kira O.

AU - Myroniuk, Solomiia

AU - Nyúl, Boglárka

AU - Reitsema, Anne Margit

AU - Ryan, Michelle K.

AU - Sasin, Edyta M.

AU - Sultana, Samiah

AU - Van Veen, Kees

N1 - 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)

PY - 2022/4/8

Y1 - 2022/4/8

N2 - 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.

AB - 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.

KW - COVID-19

KW - DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem

KW - health behaviors

KW - machine learning

KW - public goods dilemma

KW - random forest

KW - social norms

UR - http://www.scopus.com/inward/record.url?scp=85127500709&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2022.100482

DO - 10.1016/j.patter.2022.100482

M3 - Article

C2 - 35282654

SN - 2666-3899

VL - 3

JO - Patterns (New York, N.Y.)

JF - Patterns (New York, N.Y.)

IS - 4

M1 - 100482

ER -

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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