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

Author summary Human behavior and infectious diseases are linked by a feedback loop. While individuals might change their behavior as a response to an epidemic, such changes might influence the spreading itself. So far, our understanding and characterization of behavioral changes induced by diseases has been strongly limited by the lack of empirical data. As result, the vast majority of research has been focused on theoretical, what if, scenarios. In this work, we collected a unique dataset comprised of 599 surveys submitted by 434 users to the participatory surveillance platform Influweb over the 2017 - 18 and 2018 - 19 flu seasons. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of self-initiated behavioral changes implemented by each participant. Our analysis, conducted adopting machine learning algorithms, show that both past experience of illness and personal beliefs about the disease are fundamental drivers of behavioral change. These findings are in good agreement with the constructs of the Health Belief Model and provide, to the best of our knowledge, the first data driven characterization of behavioral changes during 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 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.