A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic
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-prote...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10103659/ https://www.ncbi.nlm.nih.gov/pubmed/37059871 http://dx.doi.org/10.1038/s41598-023-33033-1 |
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author | Taye, Alemayehu D. Borga, Liyousew G. Greiff, Samuel Vögele, Claus D’Ambrosio, Conchita |
author_facet | Taye, Alemayehu D. Borga, Liyousew G. Greiff, Samuel Vögele, Claus D’Ambrosio, Conchita |
author_sort | Taye, Alemayehu D. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10103659 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101036592023-04-16 A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic Taye, Alemayehu D. Borga, Liyousew G. Greiff, Samuel Vögele, Claus D’Ambrosio, Conchita Sci Rep Article 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. Nature Publishing Group UK 2023-04-14 /pmc/articles/PMC10103659/ /pubmed/37059871 http://dx.doi.org/10.1038/s41598-023-33033-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Taye, Alemayehu D. Borga, Liyousew G. Greiff, Samuel Vögele, Claus D’Ambrosio, Conchita A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title | A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title_full | A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title_fullStr | A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title_full_unstemmed | A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title_short | A machine learning approach to predict self-protecting behaviors during the early wave of the COVID-19 pandemic |
title_sort | machine learning approach to predict self-protecting behaviors during the early wave of the covid-19 pandemic |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10103659/ https://www.ncbi.nlm.nih.gov/pubmed/37059871 http://dx.doi.org/10.1038/s41598-023-33033-1 |
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