tetano
Editor, Senior Moderator
EPJ Data Sci. 2017;6(1):27. doi: 10.1140/epjds/s13688-017-0124-6. Epub 2017 Oct 24. [h=1]Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors.[/h]
Barlacchi G[SUP]1,[/SUP][SUP]2[/SUP], Perentis C[SUP]3[/SUP], Mehrotra A[SUP]4,[/SUP][SUP]5[/SUP], Musolesi M[SUP]4,[/SUP][SUP]5[/SUP], Lepri B[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
? The Author(s) 2017.
[h=4]KEYWORDS:[/h] computational health; human mobility; predictive models
PMID: 32010545 PMCID: PMC6959395 DOI: 10.1140/epjds/s13688-017-0124-6
Barlacchi G[SUP]1,[/SUP][SUP]2[/SUP], Perentis C[SUP]3[/SUP], Mehrotra A[SUP]4,[/SUP][SUP]5[/SUP], Musolesi M[SUP]4,[/SUP][SUP]5[/SUP], Lepri B[SUP]3[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
? The Author(s) 2017.
[h=4]KEYWORDS:[/h] computational health; human mobility; predictive models
PMID: 32010545 PMCID: PMC6959395 DOI: 10.1140/epjds/s13688-017-0124-6