2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
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ISDS 2014 Conference Abstracts

Wikipedia Usage Estimates Prevalence of Influenza-like 
Illness in Near Real-Time

David McIver*1, 2 and John S. Brownstein1, 2

1Boston Children’s Hospital, Boston, MA, USA; 2Harvard Medical School, Boston, MA, USA

Objective
The purpose of this work was to develop a novel method of 

estimating the amount of influenza-like illness (ILI) in a population, 
in near-real time, by using a source of information that is completely 
open to the public and free to access. We investigated the usefulness 
of data gathered from Wikipedia to estimate the prevalence of ILI in 
the United States, using data from the Centers for Disease Control and 
Prevention (CDC) as well as Google Flu Trends.

Introduction
Each year, there are an estimated 250,000–500,000 deaths 

worldwide that are attributed to seasonal influenza, with anywhere 
between 3,000–50,000 deaths occurring in the United States of 
America (US). In the US, the Centers for Disease Control and 
Prevention (CDC) continuously monitors the level of influenza-like 
illness (ILI) circulating in the population. While the CDC ILI data is 
considered to be a useful indicator of influenza activity, its availability 
has a known lag-time of between 7–14 days. To appropriately 
distribute vaccines, staff, and other healthcare commodities, it is 
critical to have up-to-date information about the prevalence of ILI 
in a population.

To this end, we have created a method of estimating current ILI activity 
in the US by gathering information on the number of times particular 
Wikipedia articles have been viewed. Not only is the information held 
within Wikipedia articles very useful on its own, but statistics and 
trends surrounding the amount of usage of particular articles, frequency 
of article edits, region specific statistics, and countless other factors 
make the Wikipedia environment an area of interest for researchers. 
Furthermore, Wikipedia makes all of this information public and freely 
available, greatly increasing and expediting any potential research 
studies that aim to make use of their data.

Methods
Data was collected on how often, with results up to the hour, 

selected Wikipedia articles were viewed. This information was 
compared to both official CDC ILI data and Google Flu Trends GFT 
data. A Poisson model was created which estimated ILI prevalence 
using all of the selected Wikipedia articles, and a separate model was 
produced using Lasso regression that automatically and dynamically 
selects the most appropriate Wikipedia articles to fit the data. A split-
sample analysis was used to test the reliability of the Lasso model, 
comparing half of the data representing the 2007-2010 flu seasons to 
the second half representing the 2011-2013 flu seasons.

Results
Our Wikipedia-based Poisson model accurately estimates the level 

of ILI activity in the American population, up to two weeks ahead 
of the CDC, with an absolute average difference between the two 
estimates of just 0.27% over 294 weeks of data. Wikipedia-derived 
ILI models performed well through both abnormally high media 
coverage events (such as during the 2009 H1N1 pandemic) as well as 
unusually severe influenza seasons (such as the 2012–2013 influenza 
season). Wikipedia usage accurately estimated the week of peak ILI 
activity 17% more often than Google Flu Trends data and was often 
more accurate in its measure of ILI intensity.

Conclusions
This study is unique in that it is the first scientific investigation 

into the harnessing of Wikipedia usage data over time to estimate the 
burden of disease in a population. The application of Wikipedia article 
view data has been demonstrated to be effective at estimating the level 
of ILI activity in the US, when compared to CDC data. Wikipedia 
article view data is available daily (and hourly, if necessary), and can 
provide a reliable estimate of ILI activity up to 2 weeks in advance of 
traditional ILI reporting. This study exemplifies how non-traditional 
data sources may be tapped to provide valuable public health 
related insights and, with further improvement and validation, could 
potentially be implemented as an automatic sentinel surveillance 
system for any number of disease or conditions of interest as a 
supplement to more traditional surveillance systems.

Keywords
Surveillance; Influenza; Digital Disease Detection

*David McIver

E-mail: david.mciver@childrens.harvard.edu    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e40, 201