ArticlePloS one2013
Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Web-based infectious disease surveillance systems and public health perspectives: a systematic review.BMC public health · 2016Pooled it
- EpiGIS pro: an AI-powered geospatial intelligence platform for integrated disease surveillance and predictive analytics.International journal of health geographics · 2026Article
- Early Detection Intervals for Evaluating Event-Based Surveillance System: Reference Dataset Development Study.JMIR public health and surveillance · 2026Article
- Design and implementation of an evaluation framework for the Epidemic Intelligence from Open Sources (EIOS) system for international public health intelligence at the Robert Koch Institute, Germany, 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Article
- Highly pathogenic avian influenza: a One Health perspective on risks, surveillance, and resilience.ImmunoHorizons · 2025Review
- Monitoring and Early Warning System: Regional Monitoring Strategy in Lombardy Region.Epidemiologia (Basel, Switzerland) · 2025Article
- Article
- Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study.BMC public health · 2023Article
- Dissemination of information in event-based surveillance, a case study of Avian Influenza.PloS one · 2023Article
- Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.JMIR public health and surveillance · 2022Article
- PADI-web 3.0: A new framework for extracting and disseminating fine-grained information from the news for animal disease surveillance.One health (Amsterdam, Netherlands) · 2021Article
- Implementing epidemic intelligence in the WHO African region for early detection and response to acute public health events.Epidemiology and infection · 2021Article
- Rumor surveillance methods in outbreaks: A systematic literature review.Health promotion perspectives · 2021Review
- Public health preparedness and responses to the coronavirus disease 2019 (COVID-19) pandemic in South Asia: a situation and policy analysis.Global health journal (Amsterdam, Netherlands) · 2020Article
- A call for an ethical framework when using social media data for artificial intelligence applications in public health research.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
- Application of natural language processing algorithms for extracting information from news articles in event-based surveillance.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
- A systematic review on integration mechanisms in human and animal health surveillance systems with a view to addressing global health security threats.One health outlook · 2020Review
- Development of a global infectious disease activity database using natural language processing, machine learning, and human expertise.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2019Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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