Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic
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Authors
van Lissa, Caspar J.Stroebe, Wolfgang
vanDellen, Michelle R.
Leander, N. Pontus
Agostini, Maximilian
Draws, Tim
Grygoryshyn, Andrii
Gützgow, Ben
Kreienkamp, Jannis
Vetter, Clara S.
Abakoumkin, Georgios
Abdul Khaiyom, Jamilah Hanum
Ahmedi, Vjolica
Akkas, Handan
Almenara, Carlos A.
Atta, Mohsin
Bagci, Sabahat Cigdem
Basel, Sima
Kida, Edona Berisha
Bernardo, Allan B.I.
Buttrick, Nicholas R.
Chobthamkit, Phatthanakit
Choi, Hoon Seok
Cristea, Mioara
Csaba, Sára
Damnjanović, Kaja
Danyliuk, Ivan
Dash, Arobindu
Di Santo, Daniela
Douglas, Karen M.
Enea, Violeta
Faller, Daiane Gracieli
Fitzsimons, Gavan J.
Gheorghiu, Alexandra
Gómez, Ángel
Hamaidia, Ali
Han, Qing
Helmy, Mai
Hudiyana, Joevarian
Jeronimus, Bertus F.
Jiang, Ding Yu
Jovanović, Veljko
Kamenov, Željka
Kende, Anna
Keng, Shian Ling
Thanh Kieu, Tra Thi
Koc, Yasin
Kovyazina, Kamila
Kozytska, Inna
Krause, Joshua
Kruglanksi, Arie W.
Kurapov, Anton
Kutlaca, Maja
Lantos, Nóra Anna
Lemay, Edward P.
Jaya Lesmana, Cokorda Bagus
Louis, Winnifred R.
Lueders, Adrian
Malik, Najma Iqbal
Martinez, Anton P.
McCabe, Kira O.
Mehulić, Jasmina
Milla, Mirra Noor
Mohammed, Idris
Molinario, Erica
Moyano, Manuel
Muhammad, Hayat
Mula, Silvana
Muluk, Hamdi
Myroniuk, Solomiia
Najafi, Reza
Nisa, Claudia F.
Nyúl, Boglárka
O'Keefe, Paul A.
Olivas Osuna, Jose Javier
Osin, Evgeny N.
Park, Joonha
Pica, Gennaro
Pierro, Antonio
Rees, Jonas H.
Reitsema, Anne Margit
Resta, Elena
Rullo, Marika
Ryan, Michelle K.
Samekin, Adil
Santtila, Pekka
Sasin, Edyta M.
Schumpe, Birga M.
Selim, Heyla A.
Stanton, Michael Vicente
Sultana, Samiah
Sutton, Robbie M.
Tseliou, Eleftheria
Utsugi, Akira
Anne van Breen, Jolien
van Veen, Kees
Vázquez, Alexandra
Wollast, Robin
Wai-Lan Yeung, Victoria
Zand, Somayeh
Issue Date
2022-04-08
Metadata
Show full item recordPublisher
Cell PressJournal
PatternsDOI
10.1016/j.patter.2022.100482Additional Links
https://www.cell.com/patterns/fulltext/S2666-3899(22)00067-8Abstract
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.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 International
Language
engEISSN
26663899Sponsors
New York University Abu Dhabiae974a485f413a2113503eed53cd6c53
10.1016/j.patter.2022.100482
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- Creative Commons