Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

Abstract

We present a machine learning-based methodology capable of providing real-time (“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC’s ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013–2014 (retrospective) and 2014–2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method’s predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT’s real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.

Accurate estimation of influenza epidemics using Google search data via ARGO

“…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). After the initial submission of this article in May 2015, Google announced that GFT would be discontinued and that their raw data would be made accessible to selected scientific teams.…”

Some Innovative Approaches for Public Health and Epidemiology Informatics

“…Early detection of disease outbreaks is not a new issue but it remains crucial. In 2015, still many researchers [8] [9][10] tried to develop the ultimate surveillance system using search engines, social network, or wiki data. While authors combined more and more data sources for their surveillance systems, the use of health system data remained limited.…”

“…However, their prediction model provides good estimates. Santillana et al [9] evaluated with promising results the same data sources, as well as some others, against the Center for Disease Control's (CDC) gold standard for influenza-like illness surveillance. Lejeune et al [10] proposed an innovative way to detect epidemic events in news articles based on string character repetition.…”

Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast

“…The first is MEAN, the average distribution seen in the training data. The second are SVMs with linear kernels, which worked well in predicting influenza activity in a similar set-up (Santillana et al, 2015). For the baselines, we encode the distributions from the 4 previous days as a flattened 12-dim.…”

“…A body of work has also used predictive signals in Twitter to track and sense upcoming unrest and protests in specific countries (Ramakrishnan et al, 2014;Goode et al, 2015), and the future progression of flu activity based on multiple text sources (Santillana et al, 2015). In contrast, we focus on predicting the sentiment dynamics in social media based on previous trends.…”