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

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. / Van Lissa, Caspar J.; Gützkow, Ben; vanDellen, Michelle R. et al.

In: Patterns, Vol. 3, No. 4, 100482, 08.04.2022.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Van Lissa, CJ, Gützkow, B, vanDellen, MR, Dash, A, Draws, T, Stroebe, W, Leander, NP, Agostini, M, Grygoryshyn, A, 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, Di Santo, D, Douglas, KM, Enea, V, Faller, DG, Fitzsimons, G, Gheorghiu, A, Gómez, Á, Hamaidia, A, Han, Q, Helmy, M, Hudiyana, J, Jeronimus, BF, Jiang, DY, Jovanović, V, Kamenov, Ž, Kende, A, Keng, SL, 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, A, 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, 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, Vázquez, A, Wollast, R, Yeung, VWL, 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

APA

Van Lissa, C. J., Gützkow, B., vanDellen, M. R., Dash, A., Draws, T., Stroebe, W., Leander, N. P., Agostini, M., Grygoryshyn, A., 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., ... 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), [100482]. https://doi.org/10.1016/j.patter.2022.100482

Vancouver

Van Lissa CJ, Gützkow B, vanDellen MR, Dash A, Draws T, Stroebe W et al. Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic. Patterns. 2022 Apr 8;3(4):100482. doi: 10.1016/j.patter.2022.100482

Bibtex

@article{f935a4b7d05c40229b229f3af9801614,
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, Economic burden, Health behaviors, Infection risk, 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, Health sciences",
author = "{Van Lissa}, {Caspar J.} and Ben G{\"u}tzkow and vanDellen, {Michelle R.} and Arobindu Dash and Tim Draws and Wolfgang Stroebe and Leander, {N. Pontus} and Maximilian Agostini and Andrii Grygoryshyn and Jannis Kreienkamp and Vetter, {Clara S.} and Georgios Abakoumkin and {Abdul Khaiyom}, {Jamilah Hanum} and Vjollca 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 {Di Santo}, Daniela and Douglas, {Karen M.} and Violeta Enea and Faller, {Daiane G.} and Gavan Fitzsimons 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 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, {N{\'o}ra Anna} and Lemay, {Edward P.} and Lesmana, {Cokorda Bagus Jaya} and Louis, {Winnifred R.} and Adrian Lueders and Malik, {Najma Iqbal} and Anton Martinez 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{\textquoteright}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 V{\'a}zquez and Robin Wollast and Yeung, {Victoria Wai lan} 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",

}

RIS

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 - Gützkow, Ben

AU - vanDellen, Michelle R.

AU - Dash, Arobindu

AU - Draws, Tim

AU - Stroebe, Wolfgang

AU - Leander, N. Pontus

AU - Agostini, Maximilian

AU - Grygoryshyn, Andrii

AU - Kreienkamp, Jannis

AU - Vetter, Clara S.

AU - Abakoumkin, Georgios

AU - Abdul Khaiyom, Jamilah Hanum

AU - Ahmedi, Vjollca

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 - Di Santo, Daniela

AU - Douglas, Karen M.

AU - Enea, Violeta

AU - Faller, Daiane G.

AU - Fitzsimons, Gavan

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 - 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, Nóra Anna

AU - Lemay, Edward P.

AU - Lesmana, Cokorda Bagus Jaya

AU - Louis, Winnifred R.

AU - Lueders, Adrian

AU - Malik, Najma Iqbal

AU - Martinez, Anton

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 - 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 - Vázquez, Alexandra

AU - Wollast, Robin

AU - Yeung, Victoria Wai lan

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 - Economic burden

KW - Health behaviors

KW - Infection risk

KW - random forest

KW - social norms

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

KW - machine learning

KW - public goods dilemma

KW - health behaviors

KW - Health sciences

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

UR - https://www.mendeley.com/catalogue/fa2e4280-a4c2-3ffd-a3d3-efdbac8cb535/

U2 - 10.1016/j.patter.2022.100482

DO - 10.1016/j.patter.2022.100482

M3 - Journal articles

C2 - 35282654

VL - 3

JO - Patterns

JF - Patterns

SN - 2666-3899

IS - 4

M1 - 100482

ER -

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