Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States.

Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks.

Background: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.

Method And Findings: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail.

Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance.

Global capacity for emerging infectious disease detection.

The increasing number of emerging infectious disease events that have spread internationally, such as severe acute respiratory syndrome (SARS) and the 2009 pandemic A/H1N1, highlight the need for improvements in global outbreak surveillance. It is expected that the proliferation of Internet-based reports has resulted in greater communication and improved surveillance and reporting frameworks, especially with the revision of the World Health Organization's (WHO) International Health Regulations (IHR 2005), which went into force in 2007. However, there has been no global quantitative assessment of whether and how outbreak detection and communication processes have actually changed over time.

Use of unstructured event-based reports for global infectious disease surveillance.

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.

Syndromic surveillance for influenzalike illness.

Emergency department data are currently being used by several syndromic surveillance systems to identify outbreaks of natural or man-made illnesses, and preliminary results suggest that regular outbreaks might be detected earlier with such data than with traditional reporting. This article summarizes a retrospective study of 5 influenza seasons in Ottawa,Canada; time-series analysis was used to look for an association between consultation to the emergency department for influenzalike illness and the isolation of influenza virus in the community. The population studied included both children and adults consulting to 3 local hospitals.