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|Title:||Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events|
|Authors:||Barboza, Philippe;Vaillant, Laetitia;Mawudeku, Abla;Nelson, Noele P.;Hartley, David M.;Madoff, Lawrence C.;Linge, Jens P.;Collier, Nigel;Brownstein, John Samuel;Yangarber, Roman;Astagneau, Pascal|
|subject:||Medicine;Epidemiology;Disease Informatics;Epidemiological Methods;Infectious Disease Epidemiology;Global Health;Infectious Diseases;Viral Diseases;Influenza;Infectious Disease Control;Infectious Disease Modeling;Neglected Tropical Diseases;Travel-Associated Diseases;Tropical Diseases (Non-Neglected);Zoonoses;Public Health|
|Publisher:||Public Library of Science|
|Description:||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. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring 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 effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System 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 in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7–13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.|
|Standard no:||Barboza, Philippe, Laetitia Vaillant, Abla Mawudeku, Noele P. Nelson, David M. Hartley, Lawrence C. Madoff, Jens P. Linge, et al. 2013. Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events. PLoS ONE 8(3): e57252.|
|Appears in Collections:||HMS Scholarly Articles|
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