A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> The recent SARS epidemic has renewed widespread concerns about the global transmission of infectious diseases. In this commentary, we explore novel approaches to global infectious disease surveillance through a focus on an important Canadian contribution to the area–the Global Public Health Intelligence Network (GPHIN). GPHIN is a cutting-edge initiative that draws on the capacity of the Internet and newly available 24/7 global news coverage of health events to create a unique form of early warning outbreak detection. <s> BIB001 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. ::: ::: Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org. ::: ::: Contact: pj.ca.iin@reilloc <s> BIB002 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> John Brownstein and colleagues discuss HealthMap, an automated real-time system that monitors and disseminates online information about emerging infectious diseases. <s> BIB003 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting. <s> BIB004 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> Google Flu Trends can detect regional outbreaks of influenza 7–10 days before conventional Centers for Disease Control and Prevention surveillance systems. We describe the Google Trends tool, explain how the data are processed, present examples, and discuss its strengths and limitations. Google Trends shows great promise as a timely, robust, and sensitive surveillance system. It is best used for surveillance of epidemics and diseases with high prevalences and is currently better suited to track disease activity in developed countries, because to be most effective, it requires large populations of Web search users. Spikes in search volume are currently hard to interpret but have the benefit of increasing vigilance. Google should work with public health care practitioners to develop specialized tools, using Google Flu Trends as a blueprint, to track infectious diseases. Suitable Web search query proxies for diseases need to be established for specialized tools or syndromic surveillance. This unique and innovative technology takes us one step closer to true real-time outbreak surveillance. <s> BIB005 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users. <s> BIB006 </s> A Survey of Social Web Mining Applications for Disease Outbreak Detection <s> Epidemic Intelligence Systems for Public Healthcare <s> Text mining for global health surveillance is an emerging technology that is gaining increased attention from public health organisations and governments. The lack of multilingual resources such as WordNets specifically targeted at this task have so far been a major bottleneck. This paper reports on a major upgrade to the BioCaster Web monitoring system and its freely available multilingual ontology; improving its original design and extending its coverage of diseases from 70 to 336 in 12 languages. <s> BIB007
Nowadays, large amounts of emergency and health data are increasingly coming from a large range of web and social media sources. This information can be very useful for disease surveillance and early outbreak detection, and several public web surveillance projects in this field have emerged over the recent years. One of the earliest surveillance systems is the Global Public Health Intelligence Network (GPHIN) BIB001 developed by Public Health Agency of Canada in collaboration with WHO. It is a secure web-based multilingual warning tool that is continuously monitoring and analysing global media data sources to identify information about disease outbreaks and other events related to public healthcare. The information is filtered for relevancy by an automated process, and categorized based on a specific taxonomy of categories. Then this information is analysed by Public Health Agency of Canada GPHIN officials. From 2002 to 2003 years, this surveillance system was able to detect the outbreak of Severe Acute Respiratory Syndrome (SARS). Since 2006, BioCaster BIB002 is an operational ontology-based system for monitoring online media data. This system is based on text mining techniques for detecting and tracking the infectious disease outbreaks through the search of linguistic signals. The system continuously analyses documents reported from over 1700 RSS feeds, Google News, WHO, ProMED-mail, and the European Media Monitor, among others providers. The extracted text are classified for topical relevance and plots them onto a Google map using geo-information. The system consists of four main stages: topic classification, named entity recognition(NER), disease/location detection and event recognition. In the first stage, the texts are classified into relevant or non-relevant categories using to train a naive Bayes classifier. Then, for relevant document corpus are search entities of interest from 18 concept types based on the BioCaster ontology BIB007 related to diseases, viruses, bacteria, locations and symptoms, see Figure 1 . HealthMap project BIB003 is a global disease alert map which uses data from different sources such as Google News, expert-curated discussion such as ProMED-mail, and official organization reports such as World Health Organization (WHO) or Euro Surveillance. This is an automated real-time system that monitors, organizes, integrates, filters, visualizes, and disseminates online information about emerging diseases as can be seen in Figure 2 . Other system which collects news from the Web, related to human and animal health, and plot the data on a Google Maps mashupare is EpiSpider BIB004 . This tool automatically extracts infectious disease outbreak information from several sources including ProMed-mail and medical web sites, and it is used as surveillance system by public healthcare organizations, several universities and health research organization. Additonally, this system automatically converted the topic and location information of the reports into RSS feeds. Other public health surveillance system used by a Public Health Organization (The European Centre of Disease Prevention and Control) is MedISys  monitoring human and animal infectious diseases, as well as chemical, biological, radiological and nuclear (CBRN) threats in open-source media. MedISys automatically collects articles concerning public health in various languages from news, which are classified according to pre-defined categories as can be seen in Figure 3 . Users can display world maps in which event locations are highlighted as well as statistics on the reporting about diseases, countries and combinations of them, also can apply filters for language, disease or location. A specific and extensive application of predictive analytic techniques to public health approach are the monitoring systems of influenza through Web and social media. Google Flu Trends BIB005 uses Google search data to estimate flu activity during two weeks giving an early detection of disease activity, see Figure 4 . This web service correlates search term frequency with influenza statistics reported by the Centers for Disease Control and Prevention (CDC), and it enables a quicker response in a potential pandemic of influenza, thus reducing its impact. Internet users perform search queries BIB006 and post entries in blogs using terms related to influenza illness as its diagnosis and symptoms. An increase or decrease in the number of illness searches and posts in blogs, reflects a higher or lower potential outbreak focus for influenza illness and can therefore be used to monitor it. Finally all the systems mentioned together with their main characteristics are listed in Table 1 .