Paper Info
Title
Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors.
Abstract
Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion.
Year
DOI
Venue
2017
10.1140/epjds/s13688-017-0124-6
EPJ Data Sci.
Keywords
Field
DocType
computational health,human mobility,predictive models
Data science,Public health,Influenza-like symptoms,Disease,Cold symptoms,Sore throat,Computer science,Simulation,Malaise,Mobile phone,Physical medicine and rehabilitation
Journal
Volume
Issue
ISSN
6
1
2193-1127
Citations 
PageRank 
References 
3
0.41
22
Authors
5
Name
Order
Citations
PageRank
Gianni Barlacchi1215.59
Christos Perentis2172.01
Abhinav Mehrotra316911.69
Mirco Musolesi43365204.65
Bruno Lepri598172.52