Infoveillance is the type of syndromic surveillance that utilizes the online contents. The term was coined by Gunther Eysenbach in 2004 for the first time along with Infodemiology
Contents
The work of Gunther Eysenbach, which utilized the Google Search queries, had led to the birth of Google Flu. Other than Google search engines have also been used. Later other researchers utilized other social media such as Twitter to find the disease outbreak patterns. The infoveillance detects disease outbreaks quicker than traditional public health surveillance systems with the minimal cost involved, revealing the promising results for the future surveillance methodologies.
Google Flu Trends
Google uses the query information to detect the flu trends and it compares the results to the countries' official surveillance data. The primary research behind the Google Flu Trend is found here. In light of evidence showing that Google Flu Trends was occasionally over-estimating flu rates, researchers have also proposed a series of more advanced and better-performing approaches to flu modelling from Google search queries.
Google Dengue Trends
Google uses the query information to detect the dengue trendsand it compares the results to the countries' official surveillance data. The primary research behind the Google Dengue Trend is found here.
Flu Detector
Flu Detector was developed by Vasileios Lampos et al. at the University of Bristol. It is an application of Machine Learning that firstly uses Feature Selection to automatically extract flu-related terms from Twitter content and then uses those terms to compute a flu-score for several UK regions based on geolocated tweets. The primary research behind the Flu Detector is found here; a generalised scheme able to track other events as well is proposed here.
A new, totally revamped (in terms of models and online data) version of the Flu Detector has been recently launched.
Mood of the Nation
Mood of the Nation was developed by Vasileios Lampos et al. at the University of Bristol. It performs mood analysis on tweets geo-located in various regions of the United Kingdom computing on a daily basis scores for four types of emotion: anger, fear, joy and sadness.