A New Online Resource to Monitor New or Emerging Plant Pests: MEDISYS Media Monitoring and the Case of Xylella fastidiosa.

The European Food Safety Authority has established a horizon scanning exercise for plant pests by automated monitoring of open-source media. The news sources are screened using the publicly accessible MEDISYS (Medical Information System) platform of the Joint Research Centre of the European Commission. Here, we report the example of monitoring for Xylella fastidiosa, a highly polyphagous plant-pathogenic bacterium. Since its first occurrence in Europe, news sources have reported findings and latest developments. Media monitoring-related data can support surveillance or plant pests' management programs by early warning and can help understand the impacts of plant pests and the societal response to new plant health threats

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

Event-based surveillance during EXPO Milan 2015. Rationale, tools, procedures, and initial results

More than 21 million participants attended EXPO Milan from May to October 2015, making it one of the largest protracted mass gathering events in Europe. Given the expected national and international population movement and health security issues associated with this event, Italy fully implemented, for the first time, an event-based surveillance (EBS) system focusing on naturally occurring infectious diseases and the monitoring of biological agents with potential for intentional release. The system started its pilot phase in March 2015 and was fully operational between April and November 2015. In order to set the specific objectives of the EBS system, and its complementary role to indicator-based surveillance, we defined a list of priority diseases and conditions. This list was designed on the basis of the probability and possible public health impact of infectious disease transmission, existing statutory surveillance systems in place, and any surveillance enhancements during the mass gathering event. This article reports the methodology used to design the EBS system for EXPO Milan and the results of 8 months of surveillance

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

Integration of the Epidemic Intelligence from Open Sources (EIOS) and the INFORM suite: Enhancing early warning with contextual data for informed decision making

The COVID-19 pandemic event has shown how communicable diseases can spread faster and wider than ever before due to globalisation. The early detection of threats plays a crucial role to reduce their impact. It is essential to combine alert systems with contextual information for triggering adequate measures that may prevent or mitigate the risk in a timely manner. This report illustrates how early warning and rapid assessment activities can be supported by the systematic collection and analysis of publicly available information from official sources and the media.JRC.E.1-Disaster Risk Managemen

Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma

Purpose: To develop and validate a CT-based radiomics signature for the prognosis of loco-regional tumour control (LRC) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated by primary radiochemotherapy (RCTx) based on retrospective data from 6 partner sites of the German Cancer Consortium - Radiation Oncology Group (DKTK-ROG). Material and methods: Pre-treatment CT images of 318 patients with locally advanced HNSCC were col-lected. Four-hundred forty-six features were extracted from each primary tumour volume and then fil-tered through stability analysis and clustering. First, a baseline signature was developed from demographic and tumour-associated clinical parameters. This signature was then supplemented by CT imaging features. A final signature was derived using repeated 3-fold cross-validation on the discovery cohort. Performance in external validation was assessed by the concordance index (C-Index). Furthermore, calibration and patient stratification in groups with low and high risk for loco-regional recurrence were analysed. Results: For the clinical baseline signature, only the primary tumour volume was selected. The final sig-nature combined the tumour volume with two independent radiomics features. It achieved moderatel

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

Peer reviewe

L11 domain rearrangement upon binding to RNA and thiostrepton studied by NMR spectroscopy

Ribosomal proteins are assumed to stabilize specific RNA structures and promote compact folding of the large rRNA. The conformational dynamics of the protein between the bound and unbound state play an important role in the binding process. We have studied those dynamical changes in detail for the highly conserved complex between the ribosomal protein L11 and the GTPase region of 23S rRNA. The RNA domain is compactly folded into a well defined tertiary structure, which is further stabilized by the association with the C-terminal domain of the L11 protein (L11(ctd)). In addition, the N-terminal domain of L11 (L11(ntd)) is implicated in the binding of the natural thiazole antibiotic thiostrepton, which disrupts the elongation factor function. We have studied the conformation of the ribosomal protein and its dynamics by NMR in the unbound state, the RNA bound state and in the ternary complex with the RNA and thiostrepton. Our data reveal a rearrangement of the L11(ntd), placing it closer to the RNA after binding of thiostrepton, which may prevent binding of elongation factors. We propose a model for the ternary L11–RNA–thiostrepton complex that is additionally based on interaction data and conformational information of the L11 protein. The model is consistent with earlier findings and provides an explanation for the role of L11(ntd) in elongation factor binding