Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

Web-based expert systems dedicated to epidemic intelligence were developed to detect health threats. The Early Alerting and Reporting (EAR) project, launched under the Global Health Initiative, aimed at assessing the feasibility and opportunity of pooling seven of those expert systems. A qualitative survey was carried out with EAR participants to document epidemic intelligence strategies and to assess perceptions regarding the performance of participating systems. Timeliness and sensitivity were rated with high scores illustrating the overall perceived value of all systems while weaknesses were underlined especially in terms of representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events which occurred in March 2010. For the six systems for which this information was available; the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The positive predictive values (PPV) ranged from 3% to 24% and the F1-score ranged from 6% to 27%. These low scores point out false positive signals related to varying abilities of the systems to efficiently sort-out information and reduce background noise. For the seven systems sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties to develop an efficient algorithm or a single pathology. The sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating the systems’ complementarities. The average delay between the detection of the A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (CI95%, 6.7; 13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in systems designs and the potential added values and opportunities for synergy: between systems, between users and between systems and users.JRC.G.2-Global security and crisis managemen

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. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p,0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion: Although differences could result from a biosurveillance system’s conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.JRC.G.2-Global security and crisis managemen

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. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion: Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance

Use of Unstructured Event-Based Reports for Global Infectious Disease Surveillance

Free or low-cost unstructured reports offer an alternative to traditional indicator-based outbreak reporting

Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events

Peer reviewe

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, radio-nuclear (CBRN) and pandemic influenza threats. At a time when no international collaborations existed in the field of event based surveillance, EAR’s innovative approach consisted in the involvement of both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security initiative that involved the Ministries of Health of G7 countries and Mexico, the World Health Organization and the European Commission. The EAR project pooled data from seven 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 of America. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first two-year pilot phase, the EAR project was tested in its capacity to monitor biological threats proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of biological nature and did not issue false alarms. This article provides, through the findings of this initial assessment, insights on how the field of epidemic intelligence can advance through an international network and more specifically on how it was further developed in the EAR project. JRC.G.2-Global security and crisis managemen

Type and geographical distribution of events published in the BHI in 2010.

<p>Type and geographical distribution of events published in the BHI in 2010.</p

Nature and geographic distribution of events reported in InVS weekly international bulletin (BHI), 2010.

<p>Eq. Enc.  =  Equine encephalitis HF  =  Haemorrhagic fever.</p