TY - JOUR
T1 - A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic
AU - Taye, Alemayehu D.
AU - Borga, Liyousew G.
AU - Greiff, Samuel
AU - Vögele, Claus
AU - D'Ambrosio, Conchita
N1 - Funding
Financial support from the André Losch Fondation, Art2Cure, Cargolux, CINVEN Foundation and COVID-19 Foundation, under the aegis of the Fondation de Luxembourg, Fonds National de la Recherche Luxembourg (14840950-COME-HERE and PRIDE17/12252781 DRIVEN), the University of Luxembourg’s Audacity project “DSEWELL”, and the Faculty of Humanities, Education and Social Sciences is gratefully acknowledged.
© 2023. The Author(s).
PY - 2023/4/14
Y1 - 2023/4/14
N2 - 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.
AB - 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.
KW - Humans
KW - COVID-19/epidemiology
KW - Pandemics
KW - SARS-CoV-2
KW - Europe/epidemiology
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85152554827&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/37059871
U2 - 10.1038/s41598-023-33033-1
DO - 10.1038/s41598-023-33033-1
M3 - Article
C2 - 37059871
SN - 2045-2322
VL - 13
SP - 6121
JO - Scientific Reports
JF - Scientific Reports
IS - 1
ER -