Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years.
The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public
health officials. This paper describes a software system which is built upon recent developments in machine
learning and data processing to achieve this goal. The system is built from reusable modules integrated into
data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem
of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect
illness outbreaks from Twitter data and then to build up and display information about these outbreaks,
including relevant news articles, to provide situational awareness. It also provides nowcasting functionality
of current disease levels from previous clinical data combined with Twitter data.
The preliminary results are promising, with the system being able to detect outbreaks of influenza-like
illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows
that using social media data can improve prediction for multiple diseases over simply using traditional data
sources.