The Global Public Health Intelligence Network

Mawudeku, Abla; Blench, Michael; Boily, Louise; John, R St; Andraghetti, R; Ruben, Martha

doi:10.1002/9781118543504.ch37

Cited by 22 publications

(23 citation statements)

References 33 publications

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“…• Global Public Health Intelligence Network, (GPHIN) (Public Health Agency of Canada) [27] • HealthMap (Harvard-MIT Division of Health Sciences and Technology, Boston, United States) [28] • MedISys (Joint Research Centre, European Commission) [29] • PULS, Pattern-based Understanding and Learning System (University of Helsinki, Finland) [30].…”

Section: Methodsmentioning

confidence: 99%

International infectious disease surveillance during the London Olympic and Paralympic Games 2012: process and outcomes

et al. 2013

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Surveillance for possible international infectious disease threats to the Olympic and Paralympic Games in London, United Kingdom, was conducted from 2 July to 12 September 2012 by a collaborative team comprising representatives from the Health Protection Agency (Public Health England since April 2013), the European Centre for Disease Prevention and Control and the National Travel Health Network and Centre. Team members enhanced their usual international surveillance activities and undertook joint risk assessments of incidents identified as relevant through an agreed set of criteria designed for the Games and using tools developed for this purpose. Although team members responded to a range of international disease incidents as part of their routine roles during this period, no incident was identified that represented a threat to the Games. Six incidents were highlighted by the team that were likely to attract media attention and hence could generate political and public concern. Responding to such concern is an important aspect of the overall public health management of mass gathering events. The lessons learned about the process and outcomes of the enhanced international surveillance will help inform planning by future hosts of similar events.

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Section: Methodsmentioning

confidence: 99%

International infectious disease surveillance during the London Olympic and Paralympic Games 2012: process and outcomes

et al. 2013

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“…The Global Public Health Intelligence Network (GPHIN) is a partially moderated system that was developed by the Government of Canada, in collaboration with the World Health Organization, four years after ProMED (31)(32)(33). GPHIN access is restricted to agencies with health-related mandates.…”

Section: Event-based Surveillance Systemsmentioning

confidence: 99%

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Rees

Gachon

et al. 2019

CCDR

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A new generation of surveillance strategies is being developed to help detect emerging infections and to identify the increased risks of infectious disease outbreaks that are expected to occur with climate change. These surveillance strategies include event-based surveillance (EBS) systems and risk modelling. The EBS systems use open-source internet data, such as media reports, official reports, and social media (such as Twitter) to detect evidence of an emerging threat, and can be used in conjunction with conventional surveillance systems to enhance early warning of public health threats. More recently, EBS systems include artificial intelligence applications such machine learning and natural language processing to increase the speed, capacity and accuracy of filtering, classifying and analysing health-related internet data. Risk modelling uses statistical and mathematical methods to assess the severity of disease emergence and spread given factors about the host (e.g. number of reported cases), pathogen (e.g. pathogenicity) and environment (e.g. climate suitability for reservoir populations). The types of data in these models are expanding to include health-related information from open-source internet data and information on mobility patterns of humans and goods. This information is helping to identify susceptible populations and predict the pathways from which infections might spread into new areas and new countries. As a powerful addition to traditional surveillance strategies that identify what has already happened, it is anticipated that EBS systems and risk modelling will increasingly be used to inform public health actions to prevent, detect and mitigate the climate change increases in infectious diseases. Affiliations

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“…These algorithms identify potential signals of emerging public health events and filter out irrelevant data considered as "noise" (Figure 1) (7). Every day, on average, the GPHIN processes 3,000 news reports (9). Volume increases when news sources expand coverage on emerging public health events such as the recent Ebola outbreak in West Africa.…”

Section: The Gphin and Big Datamentioning

confidence: 99%

“…The capacity for velocity in the GPHIN is impressive. It operates on a near real-time, 24/7 basis (4).The GPHIN system retrieves relevant data from the news aggregators every 15 minutes and is able to complete the processing (including translation) of the data in less than one minute (9).…”

Section: Deduplication Metadata Categorization Translation Relevancymentioning

confidence: 99%

Big Data and the Global Public Health Intelligence Network (GPHIN)

Dion

Abdelmalik

Mawudeku

2015

CCDR

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Background: Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events.Objective: To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN.Findings: Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion:The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.

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The Global Public Health Intelligence Network

Cited by 22 publications

References 33 publications

International infectious disease surveillance during the London Olympic and Paralympic Games 2012: process and outcomes

International infectious disease surveillance during the London Olympic and Paralympic Games 2012: process and outcomes

Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Big Data and the Global Public Health Intelligence Network (GPHIN)

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