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

Daniela Perrotta,Benjamin Althouse,Nicola Perra,Nicolò Gozzi,Daniela Paolotti

doi:10.1371/journal.pcbi.1007879

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.

Highlights

Understanding and influencing behavioral changes are key challenges for a range of disciplines such as Medicine, Psychology, Epidemiology, Social Policy and Computational Social Science
We find that the progression of the disease, the information collected about it, the risks to affect vulnerable others are relevant factors influencing behavioral changes
Our understanding of behavioral changes induced by infectious diseases is extremely limited and anecdotal

Summary

Introduction

Understanding and influencing behavioral changes are key challenges for a range of disciplines such as Medicine, Psychology, Epidemiology, Social Policy and Computational Social Science. Even in the absence of top-down (complex) interventions, external incentives, or penalties, people may spontaneously modify their behaviors in response to different types of events. Infectious diseases are a notable example of both [5,6,7,8,9,10]. They may induce a range of governmental (i.e. top-down) and/or self-initiated (i.e. bottom-up) (re)actions such as social distancing (e.g., reduction of contacts or mobility, self-isolation, quarantine, closure of public places, bans of gatherings), use of antivirals, change of diets and of personal hygiene practices [6]

Objectives

Methods

Results

Conclusion