Twitter Improves Influenza Forecasting

Abstract

Accurate disease forecasts are imperative when preparing for influenza epidemic outbreaks; nevertheless, these forecasts are often limited by the time required to collect new, accurate data. In this paper, we show that data from the microblogging community Twitter significantly improves influenza forecasting. Most prior influenza forecast models are tested against historical influenza-like illness (ILI) data from the U.S. Centers for Disease Control and Prevention (CDC). These data are released with a one-week lag and are often initially inaccurate until the CDC revises them weeks later. Since previous studies utilize the final, revised data in evaluation, their evaluations do not properly determine the effectiveness of forecasting. Our experiments using ILI data available at the time of the forecast show that models incorporating data derived from Twitter can reduce forecasting error by 17-30% over a baseline that only uses historical data. For a given level of accuracy, using Twitter data produces forecasts that are two to four weeks ahead of baseline models. Additionally, we find that models using Twitter data are, on average, better predictors of influenza prevalence than are models using data from Google Flu Trends, the leading web data source.

Accurate estimation of influenza epidemics using Google search data via ARGO

“…15, (iv) estimates produced with an AR(3) autoregressive model (4,15), and (v) a naive method that simply uses the value of the prior week's CDC ILI activity level as the estimate for the current one. For fair comparison, all benchmark models (ii-iv) are dynamically trained with a 2-y moving window.…”

“…In this article, we fixed the search query terms after 2010 so as to directly compare our results with GFT, which has kept the same query terms since 2010; future application of ARGO may update search terms more frequently. ARGO can be easily generalized to any temporal and spatial scales for a variety of diseases or social events amenable to be tracked by Internet searches or services (3,4,8,9,29,30,38,39). Further improvements in influenza prediction may come from combining multiple predictors constructed from disparate data sources (40).…”

Leveraging hospital big data to monitor flu epidemics

“…Many studies have assessed the use of internet-user activity data because they can produce real-time indicators [10][11][12][13][14][15][16][17][18]. Several data sources have been explored, including Wikipedia, Twitter or Google search-engine data.…”

Twitter Geolocation Prediction Using Neural Networks

“…It can be used for a wide range of use cases, such as monitoring of fire- (Paul et al 2014) and flue-outbreaks (Power et al 2013), provide location-based recommendations (Ye et al 2010), or is utilized in demographic analyses (Sloan et al 2013). Although some platforms, such as Twitter, allow users to geolocate posts, Jurgens et al (2015) reported that less than 3% of all Twitter posts are geotagged.…”