ArticleScientific reports2023
A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Composite socio-environmental risk score for cardiovascular assessment: An explainable machine learning approach.American journal of preventive cardiology · 2025Article
- Article
- Change in prevalence of smoking during the first year of the COVID-19 pandemic among middle-aged and older adults in Canada: a cohort study of the Canadian Longitudinal Study on Aging.BMJ public health · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Exploring the predictors affecting the sense of community of Korean high school students: application of random forests and SHAP.Frontiers in psychology · 2024Article
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5 authors.
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Abstract
Using a unique harmonized real-time data set from the COME-HERE longitudinal survey that covers five European countries (France, Germany, Italy, Spain, and Sweden) and applying a non-parametric machine learning model, this paper identifies the main individual and macro-level predictors of self-protecting behaviors against the coronavirus disease 2019 (COVID-19) during the first wave of the pandemic. Exploiting the interpretability of a Random Forest algorithm via Shapely values, we find that a higher regional incidence of COVID-19 triggers higher levels of self-protective behavior, as does a stricter government policy response. The level of individual knowledge about the pandemic, confidence in institutions, and population density also ranks high among the factors that predict self-protecting behaviors. We also identify a steep socioeconomic gradient with lower levels of self-protecting behaviors being associated with lower income and poor housing conditions. Among socio-demographic factors, gender, marital status, age, and region of residence are the main determinants of self-protective measures.
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