tetano
Editor, Senior Moderator
Sci Rep
. 2023 Apr 14;13(1):6121.
doi: 10.1038/s41598-023-33033-1.
A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic
Alemayehu D Taye[SUP] 1 [/SUP], Liyousew G Borga[SUP] 1 2 [/SUP], Samuel Greiff[SUP] 1 [/SUP], Claus Vögele[SUP] 3 [/SUP], Conchita D'Ambrosio[SUP] 1 [/SUP]
Affiliations
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.
. 2023 Apr 14;13(1):6121.
doi: 10.1038/s41598-023-33033-1.
A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic
Alemayehu D Taye[SUP] 1 [/SUP], Liyousew G Borga[SUP] 1 2 [/SUP], Samuel Greiff[SUP] 1 [/SUP], Claus Vögele[SUP] 3 [/SUP], Conchita D'Ambrosio[SUP] 1 [/SUP]
Affiliations
- PMID: 37059871
- DOI: 10.1038/s41598-023-33033-1
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.