Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

Journal: PloS one

Volume: 17

Issue: 11

Year of Publication: 2022

Affiliated Institutions:  Doctoral School of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary. Ashland University, Ashland, Ohio, United States of America. Department of Networked Systems and Services, Budapest University of Technology and Economics, Budapest, Hungary. Department of Psychology and Psychotherapy, Witten/Herdecke University, Witten, Germany. Department of Psychology, Educational Science and Human Movement, University of Palermo, Palermo, Italy. ISCTE-University Institute of Lisbon, CIS-IUL, Lisboa, Portugal. National Research University Higher School of Economics, Moscow, Russian Federation. Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki, Nigeria. Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria. Division Psychological Methodology, Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria. Institute of Psychology, Faculty of Arts, University of Presov, Prešov, Slovakia. Department of Psychology, University of Birmingham, Birmingham, United Kingdom. Institute of Psychology, Faculty of Philosophy, Jagiellonian University, Krakow, Poland. Faculty of Education, Charles University, Prague, Czech Republic. FOM University of Applied Sciences, Essen, Germany. Programa de Investigación Asociativa (PIA) en Ciencias Cognitivas, Centro de Investigación en Ciencias Cognitivas (CICC), Facultad de Psicología, Universidad de Talca, Talca, Chile. Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary.

Abstract summary 

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someone had left their home during a one week period during which they were asked to voluntarily isolate themselves. The analyses indicated that overall, an increase in the feeling of being caged leads to an increased probability of leaving home. In addition, an increased feeling of responsibility and an increased fear of getting infected decreased the probability of leaving home. The models predicted compliance behavior with between 54% and 91% accuracy within each country's sample. In addition, we modeled factors leading to risky behavior in the pandemic context. We observed an increased probability of visiting risky places as both the anticipated number of people and the importance of the activity increased. Conversely, the probability of visiting risky places increased as the perceived putative effectiveness of social distancing decreased. The variance explained in our models predicting risk ranged from < .01 to .54 by country. Together, our findings can inform behavioral interventions to increase adherence to lockdown recommendations in pandemic conditions.

Authors & Co-authors:  Hajdu Nandor N Schmidt Kathleen K Acs Gergely G Röer Jan P JP Mirisola Alberto A Giammusso Isabella I Arriaga Patrícia P Ribeiro Rafael R Dubrov Dmitrii D Grigoryev Dmitry D Arinze Nwadiogo C NC Voracek Martin M Stieger Stefan S Adamkovic Matus M Elsherif Mahmoud M Kern Bettina M J BMJ Barzykowski Krystian K Ilczuk Ewa E Martončik Marcel M Ropovik Ivan I Ruiz-Fernandez Susana S Baník Gabriel G Ulloa José Luis JL Aczel Balazs B Szaszi Barnabas B

Study Outcome 

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Statistics
Citations :  Yang X. Does city lockdown prevent the spread of COVID-19? New evidence from the synthetic control method. glob health res policy. 2021. Dec;6(1):20. doi: 10.1186/s41256-021-00204-4
Authors :  25
Identifiers
Doi : e0276970
SSN : 1932-6203
Study Population
Male,Female
Mesh Terms
Humans
Other Terms
Study Design
Cross Sectional Study
Study Approach
Country of Study
Publication Country
United States