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

TL;DR

This paper introduces a machine learning ensemble approach that combines diverse data sources like social media, search queries, and hospital records to improve real-time and forecast influenza activity predictions in the US, outperforming individual data source models.

Contribution

The novel contribution is the integration of multiple influenza-like illness data sources into a single ensemble prediction model for enhanced accuracy and early forecasting.

Findings

Ensemble predictions outperform individual data source models.

The methodology predicts up to four weeks ahead with high accuracy.

Incorporating social media and crowd-sourced data improves influenza forecasts.

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