Identifying the determinants of health protective behaviors during the COVID-19 pandemic using machine learning: an analysis of six countries
Chevalier, J. M.; Stellbrink, L. M.; Steijvers, L.; Wijnen, S.; van Daalen, F.; Kojan, L.; Li, N.; Jahn, B.; Siebert, U.; Calero Valdez, A.; Hiligsmann, M.; Crutzen, R.; Dukers-Muijrers, N. H.; Kretzschmar, M. E.
Show abstract
Individuals adapt their behavior in response to infectious disease epidemics. Understanding the determinants of behavior, particularly the impact of infections themselves, can help model the feedback loop between disease and behavior in epidemic models. We combined the Imperial College London YouGov COVID-19 behavior survey with hospitalization data and the Oxford COVID-19 government response tracker stringency index to identify the key predictors of three health behaviors--social distancing, masking, and personal protective measures (e.g. handwashing)-- during an early phase of the COVID-19 pandemic in six different countries. We compared two machine learning algorithms--logistic regression with stepwise Akaike Information Criterion and extreme gradient boosting (XGBoost). Top predictors of health behavior were perceived disease severity, hospitalizations, willingness to isolate, and intervention effectiveness, across the six countries. Logistic regression and XGBoost had comparable performance. Machine learning algorithms trained on real-world data could be used to predict individual behavior uptake in agent-based network models.
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