A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic

Alemayehu D. Taye, Liyousew G. Borga, Samuel Greiff, Claus Vögele*, Conchita D'Ambrosio

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)6121
Number of pages1
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 14 Apr 2023

Keywords

  • Humans
  • COVID-19/epidemiology
  • Pandemics
  • SARS-CoV-2
  • Europe/epidemiology
  • Machine Learning

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