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Towards a data-driven characterization of behavioral changes induced by the seasonal flu

In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 5...

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Detalles Bibliográficos
Autores principales: Gozzi, Nicolò, Perrotta, Daniela, Paolotti, Daniela, Perra, Nicola
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250468/
https://www.ncbi.nlm.nih.gov/pubmed/32401809
http://dx.doi.org/10.1371/journal.pcbi.1007879
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author Gozzi, Nicolò
Perrotta, Daniela
Paolotti, Daniela
Perra, Nicola
author_facet Gozzi, Nicolò
Perrotta, Daniela
Paolotti, Daniela
Perra, Nicola
author_sort Gozzi, Nicolò
collection PubMed
description In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.
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spelling pubmed-72504682020-06-08 Towards a data-driven characterization of behavioral changes induced by the seasonal flu Gozzi, Nicolò Perrotta, Daniela Paolotti, Daniela Perra, Nicola PLoS Comput Biol Research Article In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 − 18 and 2018 − 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals’ characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases. Public Library of Science 2020-05-13 /pmc/articles/PMC7250468/ /pubmed/32401809 http://dx.doi.org/10.1371/journal.pcbi.1007879 Text en © 2020 Gozzi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gozzi, Nicolò
Perrotta, Daniela
Paolotti, Daniela
Perra, Nicola
Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title_full Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title_fullStr Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title_full_unstemmed Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title_short Towards a data-driven characterization of behavioral changes induced by the seasonal flu
title_sort towards a data-driven characterization of behavioral changes induced by the seasonal flu
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7250468/
https://www.ncbi.nlm.nih.gov/pubmed/32401809
http://dx.doi.org/10.1371/journal.pcbi.1007879
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