Gozzi, Nicolo, Perrotta, Daniela, Paolotti, Daniela and Perra, Perra ORCID: 0000-0002-5559-3064 (2020) Towards a data-driven characterization of behavioral changes induced by the seasonal flu. PLoS Computational Biology, 16 (5):e1007879. ISSN 1553-734X (doi:https://doi.org/10.1371/journal.pcbi.1007879)

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Abstract

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

Item Type:	Article
Additional Information:	© 2020 Gozzi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords:	Digital Epidemiology, Behavioral Changes
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of International Business & Economics Faculty of Business > Networks and Urban Systems Centre (NUSC)
Last Modified:	21 Oct 2020 10:05
URI:	http://gala.gre.ac.uk/id/eprint/28880

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