Paper Info
Title
A data-driven model for influenza transmission incorporating media effects.
Abstract
Numerous studies have attempted to model the effect of mass media on the transmission of diseases such as influenza; however, quantitative data on media engagement has until recently been difficult to obtain. With the recent explosion of 'big data' coming from online social media and the like, large volumes of data on a population's engagement with mass media during an epidemic are becoming available to researchers. In this study, we combine an online dataset comprising millions of shared messages relating to influenza with traditional surveillance data on flu activity to suggest a functional form for the relationship between the two. Using this data, we present a simple deterministic model for influenza dynamics incorporating media effects, and show that such a model helps explain the dynamics of historical influenza outbreaks. Furthermore, through model selection we show that the proposed media function fits historical data better than other media functions proposed in earlier studies.
Year
DOI
Venue
2016
10.1098/rsos.160481
ROYAL SOCIETY OPEN SCIENCE
Keywords
Field
DocType
epidemiology,influenza,mathematical modelling,social media,Twitter
Population,Transmission (mechanics),Social media,Data-driven,Computer science,Model selection,Mass media,Artificial intelligence,Deterministic system,Big data,Machine learning
Journal
Volume
Issue
ISSN
3
10
2054-5703
Citations 
PageRank 
References 
0
0.34
12
Authors
2
Name
Order
Citations
PageRank
Lewis Mitchell115517.70
Joshua V. Ross221.79