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. VAN DELLEN; N. Pontus LEANDER; Maximilian AGOSTINI; Tim DRAWS; Andrii GRYGORYSHYN; Ben GÜTZKOW; Jannis KREIENKAMP; Clara S. VETTER; Georgios ABAKOUMKIN; Jamilah Hanum ABDUL KHAIYOM; Vjollca 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 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. VAN DELLEN, N. Pontus LEANDER, Maximilian AGOSTINI, Tim DRAWS, Andrii GRYGORYSHYN, Ben GÜTZKOW, Jannis KREIENKAMP, Clara S. VETTER, Georgios ABAKOUMKIN, Jamilah Hanum ABDUL KHAIYOM, Vjollca 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 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

Lingnan University

Research output: Journal Publications › Journal Article (refereed) › peer-review

21 Citations (Scopus)

Abstract

Before vaccines for 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-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
Early online date	9 Mar 2022
DOIs	https://doi.org/10.1016/j.patter.2022.100482
Publication status	Published - 8 Apr 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

Funding

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.”

Keywords

Machine learning
COVID-19
Health Behaviors
Social Norms
Public Goods
Dilemma
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

UN SDGs

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

Access to Document

10.1016/j.patter.2022.100482Licence: CC BY

Cite this

VAN LISSA, C. J., STROEBE, W., VAN DELLEN, M. R., LEANDER, N. P., AGOSTINI, M., DRAWS, T., GRYGORYSHYN, A., GÜTZKOW, 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., BERISHA KIDA, E., ... 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{9861b8c7073646d287766fc2e30ab7db,

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 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-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 = "Machine learning, COVID-19, Health Behaviors, Social Norms, Public Goods, Dilemma, 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",

author = "{VAN LISSA}, {Caspar J.} and Wolfgang STROEBE and {VAN DELLEN}, {Michelle R.} and LEANDER, {N. Pontus} and Maximilian AGOSTINI and Tim DRAWS and Andrii GRYGORYSHYN and Ben G{\"U}TZKOW 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 {BERISHA KIDA}, Edona and BERNARDO, {Allan B.I.} and BUTTRICK, {Nicholas R.} and Phatthanakit CHOBTHAMKIT and Hoon-Seok CHOI 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 Ding-Yu JIANG and Veljko JOVANOVI{\'C} and {\v Z}eljka KAMENOV and Anna KENDE and Shian-Ling KENG 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 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 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 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 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 = "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, VAN DELLEN, MR, LEANDER, NP, AGOSTINI, M, DRAWS, T, GRYGORYSHYN, A, GÜTZKOW, B, KREIENKAMP, J, VETTER, CS, ABAKOUMKIN, G, ABDUL KHAIYOM, JH, AHMEDI, V, AKKAS, H, ALMENARA, CA, ATTA, M, BAGCI, SC, BASEL, S, BERISHA KIDA, E, 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, LESMANA, CBJ, 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, 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, J, VAN VEEN, K, VÁZQUEZ, A, WOLLAST, R, YEUNG, VW-L, 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 - VAN DELLEN, Michelle R.

AU - LEANDER, N. Pontus

AU - AGOSTINI, Maximilian

AU - DRAWS, Tim

AU - GRYGORYSHYN, Andrii

AU - GÜTZKOW, Ben

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 - BERISHA KIDA, Edona

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 - LESMANA, Cokorda Bagus Jaya

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 - 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

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.

PY - 2022/4/8

Y1 - 2022/4/8

N2 - Before vaccines for 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-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 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-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 - Machine learning

KW - COVID-19

KW - Health Behaviors

KW - Social Norms

KW - Public Goods

KW - Dilemma

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

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 - Journal Article (refereed)

C2 - 35282654

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

Bibliographical note

Funding

Keywords

UN SDGs

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