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 Gutzgow; 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. Bernardo; Nicholas R. Buttrick; Phatthanakit Chobthamkit; Hoon-Seok Choi; Mioara Cristea; Sara Csaba; Kaja Damnjanovic; Ivan Danyliuk; Arobindu Dash; Daniela Di Santo; Karen M. Douglas; Violeta Enea; Daiane Gracieli Faller; Gavan J. Fitzsimons; Alexandra Gheorghiu; Angel Gomez; Ali Hamaidia; Qing Han; Mai Helmy; Joevarian Hudiyana; Bertus F. Jeronimus; Ding-Yu Jiang; Veljko Jovanovic; Zeljka 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; Nora Anna Lantos; Edward P. Lemay; Cokorda Bagus Jaya Lesmana; Winnifred R. Louis; Adrian Lueders; Najma Iqbal Malik; Anton P. Martinez; Kira O. McCabe; Jasmina Mehulic; Mirra Noor Milla; Idris Mohammed; Erica Molinario; Manuel Moyano; Hayat Muhammad; Silvana Mula; Hamdi Muluk; Solomiia Myroniuk; Reza Najafi; Claudia F. Nisa; Boglarka Nyul; 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 Vazquez; Robin Wollast; Victoria Wai-Lan Yeung; Somayeh Zand; Iris Lav Zezelj; Bang Zheng; Andreas Zick; Claudia Zuniga; Jocelyn J. Belanger

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 Gutzgow
, 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. Bernardo

Nicholas R. Buttrick, Phatthanakit Chobthamkit, Hoon-Seok Choi, Mioara Cristea, Sara Csaba, Kaja Damnjanovic, Ivan Danyliuk, Arobindu Dash, Daniela Di Santo, Karen M. Douglas, Violeta Enea, Daiane Gracieli Faller, Gavan J. Fitzsimons, Alexandra Gheorghiu, Angel Gomez, Ali Hamaidia, Qing Han, Mai Helmy, Joevarian Hudiyana, Bertus F. Jeronimus, Ding-Yu Jiang, Veljko Jovanovic, Zeljka 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, Nora Anna Lantos, Edward P. Lemay, Cokorda Bagus Jaya Lesmana, Winnifred R. Louis, Adrian Lueders, Najma Iqbal Malik, Anton P. Martinez, Kira O. McCabe, Jasmina Mehulic, Mirra Noor Milla, Idris Mohammed, Erica Molinario, Manuel Moyano, Hayat Muhammad, Silvana Mula, Hamdi Muluk, Solomiia Myroniuk, Reza Najafi, Claudia F. Nisa, Boglarka Nyul, 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 Vazquez, Robin Wollast, Victoria Wai-Lan Yeung, Somayeh Zand, Iris Lav Zezelj, Bang Zheng, Andreas Zick, Claudia Zuniga, Jocelyn J. Belanger

^*Corresponding author for this work

Methodology and Statistics

Research output: Contribution to journal › Article › Scientific › peer-review

Abstract

In a study of 56,072 participants from 28 countries, we used a machine-learning
approach to identify the strongest predictors of COVID-19-infection-prevention behavior (pre-vaccine). Few country-level data variables predicted outcomes. Instead, individual psychological variables predicted outcomes. Injunctive norms such as believing people should engage in the behaviors and support for behavioral mandates were the strongest predictors of infection-prevention behavior. The results highlight how both data- and theory-driven approaches
can increase understanding of complex human behavior.

Original language	English
Article number	100482
Number of pages	15
Journal	Patterns (New York, N.Y.)
Volume	3
Issue number	4
DOIs	https://doi.org/10.1016/J.PATTER.2022.100482
Publication status	Published - 2022

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1016/J.PATTER.2022.100482Licence: CC BY

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Van Lissa, C. J., Stroebe, W., VanDellen, M. R., Leander, N. P., Agostini, M., Draws, T., Grygoryshyn, A., Gutzgow, B., Kreienkamp, J., Vetter, C. S., Abakoumkin, G., Khaiyom, J. H. A., Ahmedi, V., Akkas, H., Almenara, C. A., Atta, M., Bagci, S. C., Basel, S., Kida, E. B., ... Belanger, 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), Article 100482. https://doi.org/10.1016/J.PATTER.2022.100482

@article{bdd881c945d84f4c8ca53c8c24457f79,

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

abstract = "In a study of 56,072 participants from 28 countries, we used a machine-learning approach to identify the strongest predictors of COVID-19-infection-prevention behavior (pre-vaccine). Few country-level data variables predicted outcomes. Instead, individual psychological variables predicted outcomes. Injunctive norms such as believing people should engage in the behaviors and support for behavioral mandates were the strongest predictors of infection-prevention behavior. The results highlight how both data- and theory-driven approaches can increase understanding of complex human behavior.",

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 Gutzgow and Jannis Kreienkamp and Vetter, \{Clara S.\} and Georgios Abakoumkin and Khaiyom, \{Jamilah Hanum Abdul\} 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.\} and Buttrick, \{Nicholas R.\} and Phatthanakit Chobthamkit and Hoon-Seok Choi and Mioara Cristea and Sara Csaba and Kaja Damnjanovic 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 Angel Gomez and Ali Hamaidia and Qing Han and Mai Helmy and Joevarian Hudiyana and Jeronimus, \{Bertus F.\} and Ding-Yu Jiang and Veljko Jovanovic and Zeljka Kamenov and Anna Kende and Shian-Ling Keng and Kieu, \{Tra Thi Thanh\} and Yasin Koc and Kamila Kovyazina and Inna Kozytska and Joshua Krause and Kruglanksi, \{Arie W.\} and Anton Kurapov and Maja Kutlaca and Lantos, \{Nora Anna\} and Lemay, \{Edward P.\} and Lesmana, \{Cokorda Bagus Jaya\} and Louis, \{Winnifred R.\} and Adrian Lueders and Malik, \{Najma Iqbal\} and Martinez, \{Anton P.\} and McCabe, \{Kira O.\} and Jasmina Mehulic 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 Boglarka Nyul and O'Keefe, \{Paul A.\} and Osuna, \{Jose Javier Olivas\} 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 \{van Breen\}, \{Jolien Anne\} and \{Van Veen\}, Kees and Alexandra Vazquez and Robin Wollast and Yeung, \{Victoria Wai-Lan\} and Somayeh Zand and Zezelj, \{Iris Lav\} and Bang Zheng and Andreas Zick and Claudia Zuniga and Belanger, \{Jocelyn J.\}",

year = "2022",

doi = "10.1016/J.PATTER.2022.100482",

language = "English",

volume = "3",

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

number = "4",

}

Van Lissa, CJ, Stroebe, W, VanDellen, MR, Leander, NP, Agostini, M, Draws, T, Grygoryshyn, A, Gutzgow, B, Kreienkamp, J, Vetter, CS, Abakoumkin, G, Khaiyom, JHA, Ahmedi, V, Akkas, H, Almenara, CA, Atta, M, Bagci, SC, Basel, S, Kida, EB, Bernardo, AB, Buttrick, NR, Chobthamkit, P, Choi, H-S, Cristea, M, Csaba, S, Damnjanovic, K, Danyliuk, I, Dash, A, Di Santo, D, Douglas, KM, Enea, V, Faller, DG, Fitzsimons, GJ, Gheorghiu, A, Gomez, A, Hamaidia, A, Han, Q, Helmy, M, Hudiyana, J, Jeronimus, BF, Jiang, D-Y, Jovanovic, V, Kamenov, Z, Kende, A, Keng, S-L, Kieu, TTT, Koc, Y, Kovyazina, K, Kozytska, I, Krause, J, Kruglanksi, AW, Kurapov, A, Kutlaca, M, Lantos, NA, Lemay, EP, Lesmana, CBJ, Louis, WR, Lueders, A, Malik, NI, Martinez, AP, McCabe, KO, Mehulic, J, Milla, MN, Mohammed, I, Molinario, E, Moyano, M, Muhammad, H, Mula, S, Muluk, H, Myroniuk, S, Najafi, R, Nisa, CF, Nyul, B, O'Keefe, PA, Osuna, JJO, 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, van Breen, JA, Van Veen, K, Vazquez, A, Wollast, R, Yeung, VW-L, Zand, S, Zezelj, IL, Zheng, B, Zick, A, Zuniga, C & Belanger, JJ 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 - 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 - Gutzgow, Ben

AU - Kreienkamp, Jannis

AU - Vetter, Clara S.

AU - Abakoumkin, Georgios

AU - Khaiyom, Jamilah Hanum Abdul

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.

AU - Buttrick, Nicholas R.

AU - Chobthamkit, Phatthanakit

AU - Choi, Hoon-Seok

AU - Cristea, Mioara

AU - Csaba, Sara

AU - Damnjanovic, 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 - Gomez, Angel

AU - Hamaidia, Ali

AU - Han, Qing

AU - Helmy, Mai

AU - Hudiyana, Joevarian

AU - Jeronimus, Bertus F.

AU - Jiang, Ding-Yu

AU - Jovanovic, Veljko

AU - Kamenov, Zeljka

AU - Kende, Anna

AU - Keng, Shian-Ling

AU - Kieu, Tra Thi Thanh

AU - Koc, Yasin

AU - Kovyazina, Kamila

AU - Kozytska, Inna

AU - Krause, Joshua

AU - Kruglanksi, Arie W.

AU - Kurapov, Anton

AU - Kutlaca, Maja

AU - Lantos, Nora Anna

AU - Lemay, Edward P.

AU - Lesmana, Cokorda Bagus Jaya

AU - Louis, Winnifred R.

AU - Lueders, Adrian

AU - Malik, Najma Iqbal

AU - Martinez, Anton P.

AU - McCabe, Kira O.

AU - Mehulic, 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 - Nyul, Boglarka

AU - O'Keefe, Paul A.

AU - Osuna, Jose Javier Olivas

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 - van Breen, Jolien Anne

AU - Van Veen, Kees

AU - Vazquez, Alexandra

AU - Wollast, Robin

AU - Yeung, Victoria Wai-Lan

AU - Zand, Somayeh

AU - Zezelj, Iris Lav

AU - Zheng, Bang

AU - Zick, Andreas

AU - Zuniga, Claudia

AU - Belanger, Jocelyn J.

PY - 2022

Y1 - 2022

N2 - In a study of 56,072 participants from 28 countries, we used a machine-learning approach to identify the strongest predictors of COVID-19-infection-prevention behavior (pre-vaccine). Few country-level data variables predicted outcomes. Instead, individual psychological variables predicted outcomes. Injunctive norms such as believing people should engage in the behaviors and support for behavioral mandates were the strongest predictors of infection-prevention behavior. The results highlight how both data- and theory-driven approaches can increase understanding of complex human behavior.

AB - In a study of 56,072 participants from 28 countries, we used a machine-learning approach to identify the strongest predictors of COVID-19-infection-prevention behavior (pre-vaccine). Few country-level data variables predicted outcomes. Instead, individual psychological variables predicted outcomes. Injunctive norms such as believing people should engage in the behaviors and support for behavioral mandates were the strongest predictors of infection-prevention behavior. The results highlight how both data- and theory-driven approaches can increase understanding of complex human behavior.

UR - https://publons.com/wos-op/publon/51704663/

U2 - 10.1016/J.PATTER.2022.100482

DO - 10.1016/J.PATTER.2022.100482

M3 - Article

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

Abstract

UN SDGs

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