Event-based internet biosurveillance: relation to epidemiological observation

Nelson, Noele P.; Li, Yang; Reilly, Aimee; Hardin, Jessica E; Hartley, David

doi:10.1186/1742-7622-9-4

Cited by 20 publications

(19 citation statements)

References 14 publications

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“…Although for the systems for which information was available, no difference was observed, an effect of the time of posting could not be formally ruled out. Nevertheless, our findings are consistent with other studies: HealthMap detected events around 12 days before WHO publication and ProMED-mail between 2 days and 2 weeks earlier than OIE when events were detected by both sources [22], Argus detected confirmed cases of pandemic (A/H1N1) from 1 to 16 days ahead of WHO for 42 countries [40]. No timeliness differences were found between HealthMap, BioCaster and EpiSPIDER [20].…”

Section: Discussionsupporting

confidence: 91%

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

et al. 2013

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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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Section: Discussionsupporting

confidence: 91%

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

et al. 2013

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“…Individual researchers may monitor online reports or researchers may use databases that are automatically created or curated by other researchers. Examples of automated databases include HealthMap (Barboza et al, 2014;Brownstein et al, 2010;Chanlekha & Collier, 2010;Collier, 2010Collier, , 2012Freifeld, Mandl, Reis, & Brownstein, 2008;Lyon, Nunn, Grossel, & Burgman, 2012); GENI-DB (Collier & Doan, 2012); Project Argus (Nelson, Li, Reilly, Hardin, & Hartley, 2012;Torii et al, 2011); ProMed-mail (Zhang, Dang, Chen, Thurmond, & Larson, 2009); MiTAP (Zhang et al, 2009); and BioCaster (Lyon et al, 2012).…”

Section: Media Reportsmentioning

confidence: 99%

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

Schwab‐Reese¹,

Hovdestad²,

Tonmyr³

et al. 2018

Child Abuse & Neglect

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Collecting child maltreatment data is a complicated undertaking for many reasons. As a result, there is an interest by child maltreatment researchers to develop methodologies that allow for the triangulation of data sources. To better understand how social media and internet-based technologies could contribute to these approaches, we conducted a scoping review to provide an overview of social media and internet-based methodologies for health research, to report results of evaluation and validation research on these methods, and to highlight studies with potential relevance to child maltreatment research and surveillance. Many approaches were identified in the broad health literature; however, there has been limited application of these approaches to child maltreatment. The most common use was recruiting participants or engaging existing participants using online methods. From the broad health literature, social media and internet-based approaches to surveillance and epidemiologic research appear promising. Many of the approaches are relatively low cost and easy to implement without extensive infrastructure, but there are also a range of limitations for each method. Several methods have a mixed record of validation and sources of error in estimation are not yet understood or predictable. In addition to the problems relevant to other health outcomes, child maltreatment researchers face additional challenges, including the complex ethical issues associated with both internet-based and child maltreatment research. If these issues are adequately addressed, social media and internet-based technologies may be a promising approach to reducing some of the limitations in existing child maltreatment data.

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“…Media monitoring has been developed in a wide variety of fields (Nelson et al., ; Barboza et al ., ; Riccardo et al ., ). In the area of plant health, media monitoring has a broad range of applications.…”

Section: Introductionmentioning

confidence: 99%

Development and testing of the media monitoring tool MedISys for early identification and reporting of existing and emerging plant health threats

et al. 2015

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Media monitoring for emerging risks has become an essential tool in public health. This approach also has the potential to deliver early warning of emerging risks to plant health. The European Food Safety Authority (EFSA) has launched a project in collaboration with the Joint Research Centre (JRC) of the European Commission to make use of the Medical Information System (MedISys) media monitoring tool for monitoring plant health threats. This paper provides a summary of the project, which is taking place in partnership with the University of Lleida and the Institut de Recerca I Tecnologia Agroalimentàries. The four specific objectives of the project are: (1) to collate new and appropriate media and information sources, (2) to develop a multilingual ontology for the global identification of emerging new plant health threats, (3) to develop strategies to monitor (re‐)emerging plant health threats at global and regional scales, and (4) to test approaches for reporting the identified warnings to the relevant EFSA units and experts through the MedISys interface, including mapping and georeferencing. The plan and structure of the project are presented with examples of preliminary results.

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Event-based internet biosurveillance: relation to epidemiological observation

Cited by 20 publications

References 14 publications

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

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

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

Development and testing of the media monitoring tool MedISys for early identification and reporting of existing and emerging plant health threats

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