Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.
Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic.
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DOI：
10.1016/j.patter.2022.100482
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发表时间：
2022-04-08
期刊：
影响因子：
--
通讯作者：
Bélanger JJ
中科院分区：
文献类型：
--
作者：
Van Lissa CJ;Stroebe W;vanDellen MR;Leander NP;Agostini M;Draws T;Grygoryshyn A;Gützgow B;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;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 DY;Jovanović V;Kamenov Ž;Kende A;Keng SL;Thanh Kieu TT;Koc Y;Kovyazina K;Kozytska I;Krause J;Kruglanksi AW;Kurapov A;Kutlaca M;Lantos NA;Lemay EP Jr;Jaya Lesmana CB;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;Olivas Osuna JJ;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;Anne van Breen J;Van Veen K;Vázquez A;Wollast R;Wai-Lan Yeung V;Zand S;Žeželj IL;Zheng B;Zick A;Zúñiga C;Bélanger JJ
在2019冠状病毒病（COVID-19）疫苗上市之前，一系列预防感染的行为构成了减缓病毒传播的主要手段。我们的研究旨在确定这组行为的重要预测因素。虽然社会和健康心理学理论提出了一组有限的预测因素，但机器学习分析可以从更大的候选预测因素中识别相关因素。我们使用随机森林对2020年3月至5月在28个国家的56，072名参与者中进行的115种感染预防行为的候选相关性进行了排名。机器学习模型在单独的测试样本中预测了52%的感染预防行为方差，超过了健康行为心理模型的表现。结果表明，两个最重要的预测相关的个人层面的禁令规范。为了说明数据驱动的方法是如何补充理论的，一些最重要的预测因素并不是来自健康行为理论，而一些理论上得出的预测因素相对来说并不重要。我们在一项跨国研究中研究了COVID-19预防行为的预测因素与禁令规范相关的最强预测因素在没有疫苗或治疗方法的情况下，病毒遏制取决于个人对世界卫生组织建议的行为的依从性。我们使用机器学习来识别最重要的合规指标，基于大型国际心理调查和国家级二级数据。最重要的指标不是“通常的嫌疑人”，如病毒感染的个人威胁，而是强制性规范，即认为一个人的社区应该从事这种行为，社会应该采取限制性的病毒遏制措施。倾向于从事感染预防行为的人也倾向于相信，普遍遵守是战胜大流行的必要条件，这延伸到对"应该"规范的认可和对行为指令的支持。这些结果突出了通过塑造社会规范和期望进行干预的潜力。在一项针对来自28个国家的56，072名参与者的研究中，我们使用机器学习方法来确定COVID-19感染预防行为（疫苗接种前）的最强预测因素。很少有国家一级的数据变量能预测结果。相反，个人心理变量预测结果。强制性规范，如相信人们应该参与的行为和支持行为的命令是最强的预测感染预防行为。研究结果强调了数据和理论驱动的方法如何增加对复杂人类行为的理解。
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. We studied predictors of COVID-19 prevention behaviors in a cross-national study The strongest predictors related to injunctive norms In the absence of a vaccine or cure, virus containment depended on individual-level compliance with behaviors recommended by the World Health Organization. We used machine learning to identify the most important indicators of compliance, based on a large international psychological survey and on country-level secondary data. The most important indicators were not the “usual suspects,” such as personal threat of virus infection, but rather injunctive norms—namely, the belief that one’s community should engage in such behavior and that society should take restrictive virus-containment measures. People who tend to engage in infection-prevention behaviors also tend to believe that general compliance is necessary to defeat the pandemic, which extends to endorsement of “ought” norms and support for behavioral mandates. These results highlight the potential to intervene by shaping social norms and expectations. 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.
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影响因子：
6.9
作者：
Han Q;Zheng B;Cristea M;Agostini M;Bélanger JJ;Gützkow B;Kreienkamp J;PsyCorona Collaboration;Leander NP
通讯作者：
Leander NP
影响因子：
2.6
作者：
Hughes, ME;Waite, LJ;Cacioppo, JT
通讯作者：
Cacioppo, JT
影响因子：
7.6
作者：
CARVER, CS;SCHEIER, MF;WEINTRAUB, JK
通讯作者：
WEINTRAUB, JK
影响因子：
6.6
作者：
Han Q;Zheng B;Agostini M;Bélanger JJ;Gützkow B;Kreienkamp J;Reitsema AM;van Breen JA;Collaboration P;Leander NP
通讯作者：
Leander NP
影响因子：
4.3
作者：
Jin, Shuxian;Balliet, Daniel;Romano, Angelo;Spadaro, Giuliana;van Lissa, Caspar J.;Agostini, Maximilian;Belanger, Jocelyn J.;Gutzkow, Ben;Kreienkamp, Jannis;Leander, N. Pontus
通讯作者：
Leander, N. Pontus