An overview of Internet biosurveillance

Hartley, David; Nelson, Noele P.; Arthur, Ray R.; Barboza, P; Collier, Nigel; Lightfoot, N. F.; Linge, Jens P.; Goot, Erik van der; Mawudeku, Abla; Madoff, Lawrence C.; Vaillant, L.; Walters, R.A.; Yangarber, Roman; Mantero, Jas; Corley, Courtney D.; Brownstein, John S.

doi:10.1111/1469-0691.12273

Cited by 70 publications

(59 citation statements)

References 36 publications

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“…It did not take long before researchers saw the potential of these new technologies for epidemiology [640][641][642]. Indeed, the large amount of data we generate, or that we can collect, contains crucial epidemiological indicators.…”

Section: Digital Epidemiologymentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Digital Epidemiologymentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Internet biosurveillance emerged in the mid-1990s and has matured into a globally recognized technique for providing early warning of, and situational awareness for, public health threats [1][2]. This web-based approach utilizes unstructured real-time or near real-time data to support and complement traditional indicator-based surveillance.…”

Section: Background Internet Biosurveillancementioning

confidence: 99%

“…The idea of combining existing internet biosurveillance systems to create a stronger platform is echoed throughout the literature [4,[6][7]. In 2013, Hartley and colleagues suggested the use of interactive functions for users such as scoring options and comment fields [1]. In 2014, a systematic review by Velasco and colleagues assessed 13 eventbased surveillance systems from Canada, the European Union, Japan, and the United States and identified that no system had been incorporated into a national surveillance program [8].…”

Section: Background Internet Biosurveillancementioning

confidence: 99%

KIWI: A technology for public health event monitoring and early warning detection

Mukhi

Andres²,

Demianyk³

et al. 2016

OJPHI

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Objectives: To introduce the Canadian Network for Public Health Intelligence's new Knowledge Integration using Web-based Intelligence (KIWI) technology, and to pefrom preliminary evaluation of the KIWI technology using a case study. The purpose of this new technology is to support surveillance activities by monitoring unstructured data sources for the early detection and awareness of potential public health threats.Methods: A prototype of the KIWI technology, adapted for zoonotic and emerging diseases, was piloted by end-users with expertise in the field of public health and zoonotic/emerging disease surveillance. The technology was assessed using variables such as geographic coverage, user participation, and others; categorized by high-level attributes from evaluation guidelines for internet based surveillance systems. Special attention was given to the evaluation of the system's automated sense-making algorithm, which used variables such as sensitivity, specificity, and predictive values. Event-based surveillance evaluation was not applied to its full capacity as such an evaluation is beyond the scope of this paper.Results: KIWI was piloted with user participation = 85.0% and geographic coverage within monitored sources = 83.9% of countries. The pilots, which focused on zoonotic and emerging diseases, lasted a combined total of 65 days and resulted in the collection of 3243 individual information pieces (IIP) and 2 community reported events (CRE) for processing. Ten sources were monitored during the second phase of the pilot, which resulted in 545 anticipatory intelligence signals (AIS). KIWI's automated sense-making algorithm (SMA) had sensitivity = 63.9% (95% CI: 60.2-67.5%), specificity = 88.6% (95% CI: 87.3-89.8%), positive predictive value = 59.8% (95% CI: 56.1-63.4%), and negative predictive value = 90.3% (95% CI: 89.0-91.4%).Discussion: Literature suggests the need for internet based monitoring and surveillance systems that are customizable, integrated into collaborative networks of public health professionals, and incorporated into national surveillance activities. Results show that the KIWI technology is well posied to address some of the suggested challenges. A limitation of this study is that sample size for pilot participation was small for capturing overall readiness of integrating KIWI into regular surveillance activities.Conclusions: KIWI is a customizable technology developed within an already thriving collaborative platform used by public health professionals, and performs well as a tool for discipline-specific event monitoring and early warning signal detection.

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“…2,9 In addition to relying on indicator-based surveillance, which generally analyzes data routinely collected from healthcare facilities through institutional disease reporting, EI integrated the monitoring of rumors by formalizing the methodology of event-based surveillance (EBS). [10][11][12] Eventbased surveillance captures information from immediately available unstructured data, from sources that go beyond the health sector. 13 The limitation of this information is that it is often not official, not verified, abundant, and available in various formats and levels of detail.…”

mentioning

confidence: 99%

“…With the birth of event-based surveillance came the development of internet-based biosurveillance systems (IBBSs) 12,[14][15][16][17][18][19][20] aimed at detecting health threats by using information available on the internet, and the need for professional analysts able to monitor, detect, and assess potentially relevant information captured from news items, blogs, or other sources of information. 21 Between 2008 and 2010, the EAR project developed and implemented a new synergistic combined epidemic intelligence technical tool (''EAR portal''), which pooled information from 7 IBBS providers.…”

mentioning

confidence: 99%

Interfacing a Biosurveillance Portal and an International Network of Institutional Analysts to Detect Biological Threats

Riccardo¹,

ShigematsuMika²,

ChowCatherine³

et al. 2014

Biosecurity and Bioterrorism: Biodefense Strategy, Practice, an

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The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of eventbased surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the Flavia Riccardo, PhD, is a Researcher; Maria Grazia Dente, MSc, is a Senior Researcher; Silvia Declich, MSc, is a Senior Researcher; all in the National

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An overview of Internet biosurveillance

Cited by 70 publications

References 36 publications

Statistical physics of vaccination

Statistical physics of vaccination

KIWI: A technology for public health event monitoring and early warning detection

Interfacing a Biosurveillance Portal and an International Network of Institutional Analysts to Detect Biological Threats

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