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. 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; 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 Anne 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

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

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. 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 Anne 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: Contribution to journal › Article › peer-review

20 Citations (Scopus)

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
Journal	Patterns
Volume	3
Issue number	4
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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.patter.2022.100482

Cite this

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., Kida, E. B., ... 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, 3(4), Article 100482. https://doi.org/10.1016/j.patter.2022.100482

@article{35976b44b4cd4e8a8d891f1ace0a64d2,

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 = "{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 Mai Helmy and Joevarian Hudiyana and Jeronimus, {Bertus F.} and Jiang, {Ding Yu} and Veljko Jovanovi{\'c} and {\v Z}eljka Kamenov and Anna Kende and Keng, {Shian Ling} and {Thanh Kieu}, {Tra Thi} and Yasin Koc and Kamila Kovyazina and Inna Kozytska and Joshua Krause and Kruglanksi, {Arie W.} and Anton Kurapov and Maja Kutlaca and Lantos, {N{\'o}ra Anna} and Lemay, {Edward P.} and {Jaya Lesmana}, {Cokorda Bagus} and Louis, {Winnifred R.} and Adrian Lueders and Malik, {Najma Iqbal} and Martinez, {Anton P.} and McCabe, {Kira O.} and Jasmina Mehuli{\'c} and Milla, {Mirra Noor} and Idris Mohammed and Erica Molinario and Manuel Moyano and Hayat Muhammad and Silvana Mula and Hamdi Muluk and Solomiia Myroniuk and Reza Najafi and Nisa, {Claudia F.} and Bogl{\'a}rka Ny{\'u}l and O'Keefe, {Paul A.} and {Olivas Osuna}, {Jose Javier} and Osin, {Evgeny N.} and Joonha Park and Gennaro Pica and Antonio Pierro and Rees, {Jonas H.} and Reitsema, {Anne Margit} and Elena Resta and Marika Rullo and Ryan, {Michelle K.} and Adil Samekin and Pekka Santtila and Sasin, {Edyta M.} and Schumpe, {Birga M.} and Selim, {Heyla A.} and Stanton, {Michael Vicente} and Samiah Sultana and Sutton, {Robbie M.} and Eleftheria Tseliou and Akira Utsugi and {Anne van Breen}, Jolien and {Van Veen}, Kees and Alexandra V{\'a}zquez and Robin Wollast and {Wai-Lan Yeung}, Victoria and Somayeh Zand and {\v Z}e{\v z}elj, {Iris Lav} and Bang Zheng and Andreas Zick and Claudia Z{\'u}{\~n}iga and B{\'e}langer, {Jocelyn J.}",

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",

issn = "2666-3899",

publisher = "Cell Press",

number = "4",

}

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, Helmy, M, Hudiyana, J, Jeronimus, BF, Jiang, DY, Jovanović, V, Kamenov, Ž, Kende, A, Keng, SL, 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 2022, 'Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic', Patterns, 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 - 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 - Helmy, Mai

AU - Hudiyana, Joevarian

AU - Jeronimus, Bertus F.

AU - Jiang, Ding Yu

AU - Jovanović, Veljko

AU - Kamenov, Željka

AU - Kende, Anna

AU - Keng, Shian Ling

AU - Thanh Kieu, Tra Thi

AU - Koc, Yasin

AU - Kovyazina, Kamila

AU - Kozytska, Inna

AU - Krause, Joshua

AU - Kruglanksi, Arie W.

AU - Kurapov, Anton

AU - Kutlaca, Maja

AU - Lantos, Nóra Anna

AU - Lemay, Edward P.

AU - Jaya Lesmana, Cokorda Bagus

AU - Louis, Winnifred R.

AU - Lueders, Adrian

AU - Malik, Najma Iqbal

AU - Martinez, Anton P.

AU - McCabe, Kira O.

AU - Mehulić, Jasmina

AU - Milla, Mirra Noor

AU - Mohammed, Idris

AU - Molinario, Erica

AU - Moyano, Manuel

AU - Muhammad, Hayat

AU - Mula, Silvana

AU - Muluk, Hamdi

AU - Myroniuk, Solomiia

AU - Najafi, Reza

AU - Nisa, Claudia F.

AU - Nyúl, Boglárka

AU - O'Keefe, Paul A.

AU - Olivas Osuna, Jose Javier

AU - Osin, Evgeny N.

AU - Park, Joonha

AU - Pica, Gennaro

AU - Pierro, Antonio

AU - Rees, Jonas H.

AU - Reitsema, Anne Margit

AU - Resta, Elena

AU - Rullo, Marika

AU - Ryan, Michelle K.

AU - Samekin, Adil

AU - Santtila, Pekka

AU - Sasin, Edyta M.

AU - Schumpe, Birga M.

AU - Selim, Heyla A.

AU - Stanton, Michael Vicente

AU - Sultana, Samiah

AU - Sutton, Robbie M.

AU - Tseliou, Eleftheria

AU - Utsugi, Akira

AU - Anne van Breen, Jolien

AU - Van Veen, Kees

AU - Vázquez, Alexandra

AU - Wollast, Robin

AU - Wai-Lan Yeung, Victoria

AU - Zand, Somayeh

AU - Žeželj, Iris Lav

AU - Zheng, Bang

AU - Zick, Andreas

AU - Zúñiga, Claudia

AU - Bélanger, Jocelyn J.

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

AN - SCOPUS:85127500709

SN - 2666-3899

VL - 3

JO - Patterns

JF - Patterns

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

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this