Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

van Lissa CJ, Stroebe W, vanDellen MR, Leander NP, Agostini M, Draws T, Grygoryshyn A, Gutzgow B, Kreienkamp J, Vetter CS, Abakoumkin G, et al. (2022)
Patterns : 100482.

Zeitschriftenaufsatz | E-Veröff. vor dem Druck | Englisch
 
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Autor*in
van Lissa, Caspar J; Stroebe, Wolfgang; vanDellen, Michelle R; Leander, N Pontus; Agostini, Maximilian; Draws, Tim; Grygoryshyn, Andrii; Gutzgow, Ben; Kreienkamp, Jannis; Vetter, Clara S; Abakoumkin, Georgios; Abdul Khaiyom, Jamilah Hanum
Alle
Abstract / Bemerkung
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. © 2022 The Author(s).
Erscheinungsjahr
2022
Zeitschriftentitel
Patterns
Art.-Nr.
100482
ISSN
2666-3899
Page URI
https://pub.uni-bielefeld.de/record/2962043

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van Lissa CJ, Stroebe W, vanDellen MR, et al. Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns . 2022: 100482.
van Lissa, C. J., Stroebe, W., vanDellen, M. R., Leander, N. P., Agostini, M., Draws, T., Grygoryshyn, A., et al. (2022). Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns , 100482. https://doi.org/10.1016/j.patter.2022.100482
van Lissa, Caspar J, Stroebe, Wolfgang, vanDellen, Michelle R, Leander, N Pontus, Agostini, Maximilian, Draws, Tim, Grygoryshyn, Andrii, et al. 2022. “Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic”. Patterns : 100482.
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., et al. (2022). Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns :100482.
van Lissa, C.J., et al., 2022. Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns , : 100482.
C.J. van Lissa, et al., “Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic”, Patterns , 2022, : 100482.
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., Abdul Khaiyom, J.H., Ahmedi, V., Akkas, H., Almenara, C.A., Atta, M., Bagci, S.C., Basel, S., Kida, E.B., Bernardo, A.B.I., Buttrick, N.R., Chobthamkit, P., Choi, H.-S., Cristea, M., Csaba, S., Damnjanovic, K., Danyliuk, I., Dash, A., Di Santo, D., Douglas, K.M., Enea, V., Faller, D.G., Fitzsimons, G.J., Gheorghiu, A., Gomez, A., Hamaidia, A., Han, Q., Helmy, M., Hudiyana, J., Jeronimus, B.F., Jiang, D.-Y., Jovanovic, V., Kamenov, Z., Kende, A., Keng, S.-L., Thanh Kieu, T.T., Koc, Y., Kovyazina, K., Kozytska, I., Krause, J., Kruglanksi, A.W., Kurapov, A., Kutlaca, M., Lantos, N.A., Lemay, E.P.J., Jaya Lesmana, C.B., Louis, W.R., Lueders, A., Malik, N.I., Martinez, A.P., McCabe, K.O., Mehulic, J., Milla, M.N., Mohammed, I., Molinario, E., Moyano, M., Muhammad, H., Mula, S., Muluk, H., Myroniuk, S., Najafi, R., Nisa, C.F., Nyul, B., O'Keefe, P.A., Olivas Osuna, J.J., Osin, E.N., Park, J., Pica, G., Pierro, A., Rees, J., Reitsema, A.M., Resta, E., Rullo, M., Ryan, M., Samekin, A., Santtila, P., Sasin, E.M., Schumpe, B.M., Selim, H.A., Stanton, M.V., Sultana, S., Sutton, R.M., Tseliou, E., Utsugi, A., Anne van Breen, J., Van Veen, K., Vazquez, A., Wollast, R., Wai-Lan Yeung, V., Zand, S., Zezelj, I.L., Zheng, B., Zick, A., Zuniga, C., Belanger, J.J.: Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic. Patterns . : 100482 (2022).
van Lissa, Caspar J, Stroebe, Wolfgang, vanDellen, Michelle R, Leander, N Pontus, Agostini, Maximilian, Draws, Tim, Grygoryshyn, Andrii, Gutzgow, 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, Sara, Damnjanovic, Kaja, Danyliuk, Ivan, Dash, Arobindu, Di Santo, Daniela, Douglas, Karen M, Enea, Violeta, Faller, Daiane Gracieli, Fitzsimons, Gavan J, Gheorghiu, Alexandra, Gomez, Angel, Hamaidia, Ali, Han, Qing, Helmy, Mai, Hudiyana, Joevarian, Jeronimus, Bertus F, Jiang, Ding-Yu, Jovanovic, Veljko, Kamenov, Zeljka, 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, Nora Anna, Lemay, Edward P Jr, Jaya Lesmana, Cokorda Bagus, Louis, Winnifred R, Lueders, Adrian, Malik, Najma Iqbal, Martinez, Anton P, McCabe, Kira O, Mehulic, Jasmina, Milla, Mirra Noor, Mohammed, Idris, Molinario, Erica, Moyano, Manuel, Muhammad, Hayat, Mula, Silvana, Muluk, Hamdi, Myroniuk, Solomiia, Najafi, Reza, Nisa, Claudia F, Nyul, Boglarka, O'Keefe, Paul A, Olivas Osuna, Jose Javier, Osin, Evgeny N, Park, Joonha, Pica, Gennaro, Pierro, Antonio, Rees, Jonas, Reitsema, Anne Margit, Resta, Elena, Rullo, Marika, Ryan, Michelle, 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, Vazquez, Alexandra, Wollast, Robin, Wai-Lan Yeung, Victoria, Zand, Somayeh, Zezelj, Iris Lav, Zheng, Bang, Zick, Andreas, Zuniga, Claudia, and Belanger, Jocelyn J. “Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic”. Patterns (2022): 100482.

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