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Howard Burkom - One of the best experts on this subject based on the ideXlab platform.

  • a value driven framework for the evaluation of Biosurveillance systems
    Online Journal of Public Health Informatics, 2017
    Co-Authors: Victor J Del Rio Vilas, Howard Burkom, M Kocaman, Richard S Hopkins, John Berezowski, Ian Painter, Julia Gunn, Gilberto Montibeller, Matteo Convertino, Laura C Streichert
    Abstract:

    Objective To describe the development of an evaluation framework that allows quantification of surveillance functions and subsequent aggregation towards an overall score for Biosurveillance system performance. Introduction Evaluation and strengthening of Biosurveillance systems is a complex process that involves sequential decision steps, numerous stakeholders, and requires accommodating multiple and conflicting objectives. Biosurveillance evaluation, the initiating step towards Biosurveillance strengthening, is a multi-dimensional decision problem that can be properly addressed via multi-criteria-decision models. Existing evaluation frameworks tend to focus on “hard” technical attributes (e.g. sensitivity) while ignoring other “soft” criteria (e.g. transparency) of difficult measurement and aggregation. As a result, Biosurveillance value, a multi-dimensional entity, is not properly defined or assessed. Not addressing the entire range of criteria leads to partial evaluations that may fail to convene sufficient support across the stakeholders’ base for Biosurveillance improvements. We seek to develop a generic and flexible evaluation framework capable of integrating the multiple and conflicting criteria and values of different stakeholders, and which is sufficiently tractable to allow quantification of the value of specific Biosurveillance projects towards the overall performance of Biosurveillance systems. Methods We chose a Multi Attribute Value Theory model (MAVT) to support the development of the evaluation framework. Development of the model was done through online decision conferencing sessions with expert judgement, an indispensable part of MAVT modelling, provided by surveillance experts recruited from the member pool of the International Society for Disease Surveillance. The surveillance functions or quality criteria that were considered for the framework were initially gathered from a review of the literature with specific attention to a subset of public health quality criteria (1). Group discussions with the experts led to a final list of functions, finally reviewed to comply with the properties for good criteria in decision models. The eleven functions were: sensitivity; timeliness; positive predictive value (PPV); transparency; versatility; multiple utility; representativeness; sustainability; advancing the field and innovation; risk reduction; and actionable information. In addition, 24 different scenarios were developed for sensitivity, PPV, and timeliness since their values may differ with the level of infectiousness of the condition/event of interest, its severity and the availability of treatment and/or prevention measures. Four or five levels of performance were also developed for each criterion. Macbeth (Measuring Attractiveness by a Category-Based Evaluation Technique) tables were used to elicit values of different levels of performance from the experts using qualitative pairwise comparisons and then convert them into numerical values. Results To date, two criteria, sensitivity and transparency, have been assessed by more than one expert working on the same scenario. Value functions were generated for each criterion and scenario by calculating the median of the different values produced by the experts. For both sensitivity and transparency, value functions were mostly linear, indicating similar preferences between levels of performance. However, for some scenarios, experts allocated greater value to increases at the higher end of the performance level distribution. Conclusions At the time of writing new elicitation sessions are planned to conclude the model. Next, we will apply swing weights to support the trade-offs between the different criteria. We will present the baseline model elicitated from the experts and demonstrate how to apply portfolio decision analysis to assess overall performance of Biosurveillance systems according to the specific needs of stakeholders and in conjunction with macro-epidemiological models.

  • practical comparison of aberration detection algorithms for Biosurveillance systems
    Journal of Biomedical Informatics, 2015
    Co-Authors: Hong Zhou, Howard Burkom, Carla Winston, Achintya N Dey, Umed A Ajani
    Abstract:

    Display Omitted We used syndromic data from 62 VHA hospitals and simulated alerting signals.We compared 7 detection methods on sensitivity and timeliness of alerting.Weekday/weekend stratification improved detection performance.Total visit adjustment increased sensitivity and timeliness.A Poisson regression provided the best sensitivity for high-count data. National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ?1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.

  • an isds based initiative for conventions for Biosurveillance data analysis methods
    Online Journal of Public Health Informatics, 2013
    Co-Authors: Michael Coletta, Howard Burkom, Jeffrey Johnson, Wendy W Chapman
    Abstract:

    The panel will seek consensus for a plan to establish standards for technical analysis of Biosurveillance data. The scope of a conventions group could include specification of practical problems, statistical monitoring and follow-up methods, and alternative use applications such as clinical decision support. A primary goal is removal of obstacles to relevant, replicable research and to direct collaboration between public health practitioners and the academic community.

  • comments on some methodological issues in Biosurveillance
    Statistics in Medicine, 2011
    Co-Authors: Howard Burkom
    Abstract:

    Dr Fricker’s paper addresses the practical, multidisciplinary problem of Biosurveillance. This field has seen numerous books and journal articles in the past 10 years and is still evolving as a science. Based on his own reading, research, and contacts with those in public health practice (PHP), the author presents his view of the status and needed direction of this field. The remarks below are intended to augment this view from another perspective. Perspective: Following a background in mathematics and 20 years of developing and evaluating detection systems in non-health-related disciplines, I have worked on Biosurveillance systems since 2000 while acquiring knowledge in epidemiology and biostatistics related to public health. The recent years have been devoted to the design, development, and evaluation of ESSENCE Biosurveillance systems and to consulting for the U.S. Centers for Disease Control and Prevention, now my half-time employer. As a member since 2005 of the Board of Directors of the International Society for Disease Surveillance (ISDS), my role has been liaison to the ISDS Research Committee, which has an international roster of over 130 public health practitioners and academic, government, and industrial researchers. Dr Fricker’s remarks about the need for cross-disciplinary collaboration resonated with efforts over the past 2 years between the ISDS Research and PHP committees to promote research efforts with near-term PHP utility. These efforts have spawned several webinar and conference sessions, most notably a widely advertised and attended event entitled ‘Technical Challenges from the Public Health Practice Community’, whose recording and related products are freely available [1]. The remarks below are heavily influenced by the observations of PHP members from these events and by project-related contacts.

  • statistical challenges facing early outbreak detection in Biosurveillance
    Quality Engineering, 2011
    Co-Authors: Galit Shmueli, Howard Burkom
    Abstract:

    Modern Biosurveillance is the monitoring of a wide range of prediagnostic and diagnostic data for the purpose of enhancing the ability of the public health infrastructure to detect, investigate, and respond to disease outbreaks. Statistical control charts have been a central tool in classic disease surveillance and also have migrated into modern Biosurveillance; however, the new types of data monitored, the processes underlying the time series derived from these data, and the application context all deviate from the industrial setting for which these tools were originally designed. Assumptions of normality, independence, and stationarity are typically violated in syndromic time series. Target values of process parameters are time-dependent and hard to define, and data labeling is ambiguous in the sense that outbreak periods are not clearly defined or known. Additional challenges include multiplicity in several dimensions, performance evaluation, and practical system usage and requirements. Our focus is mainly on the monitoring of time series to provide early alerts of anomalies to stimulate investigation of potential outbreaks, with a brief summary of methods to detect significant spatial and spatiotemporal case clusters. We discuss the statistical challenges in monitoring modern Biosurveillance data, describe the current state of monitoring in the field, and survey the most recent Biosurveillance literature.

David M Hartley - One of the best experts on this subject based on the ideXlab platform.

  • factors influencing performance of internet based Biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks
    PLOS ONE, 2014
    Co-Authors: P Barboza, Noele P Nelson, David M Hartley, Lawrence C Madoff, Jens P Linge, Abla Mawudeku, Nigel Collier, L Vaillant, Yann Le Strat, John S Brownstein
    Abstract:

    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.

  • an overview of internet Biosurveillance
    Clinical Microbiology and Infection, 2013
    Co-Authors: Noele P Nelson, David M Hartley, Ray R Arthur, Jens P Linge, Nigel Collier, P Barboza, Nigel Lightfoot, E Van Der Goot
    Abstract:

    Internet Biosurveillance utilizes unstructured data from diverse web-based sources to provide early warning and situational awareness of public health threats. The scope of source coverage ranges from local media in the vernacular to international media in widely read languages. Internet Biosurveillance is a timely modality that is available to government and public health officials, healthcare workers, and the public and private sector, serving as a real-time complementary approach to traditional indicator-based public health disease surveillance methods. Internet Biosurveillance also supports the broader activity of epidemic intelligence. This overview covers the current state of the field of Internet Biosurveillance, and provides a perspective on the future of the field.

  • assessing the continuum of event based Biosurveillance through an operational lens
    Biosecurity and Bioterrorism-biodefense Strategy Practice and Science, 2012
    Co-Authors: Courtney D Corley, Ronald A Walters, Ray R Arthur, Mary J Lancaster, Robert T Brigantic, James S Chung, Cynthia J Brucknerlea, Augustin Calapristi, Glenn Dowling, David M Hartley
    Abstract:

    This research follows the Updated Guidelines for Evaluating Public Health Surveillance Systems, Recommendations from the Guidelines Working Group, published by the Centers for Disease Control and Prevention nearly a decade ago. Since then, models have been developed and complex systems have evolved with a breadth of disparate data to detect or forecast chemical, biological, and radiological events that have a significant impact on the One Health landscape. How the attributes identified in 2001 relate to the new range of event-based Biosurveillance technologies is unclear. This article frames the continuum of event-based Biosurveillance systems (that fuse media reports from the internet), models (ie, computational that forecast disease occurrence), and constructs (ie, descriptive analytical reports) through an operational lens (ie, aspects and attributes associated with operational considerations in the development, testing, and validation of the event-based Biosurveillance methods and models and their use...

  • event based Biosurveillance of respiratory disease in mexico 2007 2009 connection to the 2009 influenza a h1n1 pandemic
    Eurosurveillance, 2010
    Co-Authors: Noele P Nelson, John S Brownstein, David M Hartley
    Abstract:

    The emergence of the 2009 pandemic influenza A(H1N1) virus in North America and its subsequent global spread highlights the public health need for early warning of infectious disease outbreaks. Event-based Biosurveillance, based on local- and regional-level Internet media reports, is one approach to early warning as well as to situational awareness. This study analyses media reports in Mexico collected by the Argus Biosurveillance system between 1 October 2007 and 31 May 2009. Results from Mexico are compared with the United States and Canadian media reports obtained from the HealthMap system. A significant increase in reporting frequency of respiratory disease in Mexico during the 2008-9 influenza season relative to that of 2007-8 was observed (p<0.0001). The timing of events, based on media reports, suggests that respiratory disease was prevalent in parts of Mexico, and was reported as unusual, much earlier than the microbiological identification of the pandemic virus. Such observations suggest that abnormal respiratory disease frequency and severity was occurring in Mexico throughout the winter of 2008-2009, though its connection to the emergence of the 2009 pandemic influenza A(H1N1) virus remains unclear.

  • data sources for Biosurveillance
    Wiley Handbook of Science and Technology for Homeland Security, 2010
    Co-Authors: Ronald A Walters, Pete A Harlan, Noele P Nelson, David M Hartley
    Abstract:

    Biosurveillance requires a synthesis of analytic approaches derived from the natural disaster, public health, medical, meteorological, and social science communities, among others, and it is the cornerstone of early disease detection. Biosurveillance includes the analyses of timely data, often fusing time series of many different types of data, to infer the status of public health rather than solely exploiting data having diagnostic specificity. This paper summarizes major systems dedicated to such an endeavor with emphasis on those that use formal reporting sources and those that use open source collection. Although, individual systems have different missions and approaches, there are many complementarities among them that if creatively exploited could be the basis of an effective global biosuveillance enterprise. Keywords: Biosurveillance; global; alert; outbreaks; epidemic; detection; health

Alina Deshpande - One of the best experts on this subject based on the ideXlab platform.

  • fast evaluation of viral emerging risks fever a computational tool for Biosurveillance diagnostics and mutation typing of emerging viral pathogens
    medRxiv, 2021
    Co-Authors: Zachary R Stromberg, Alina Deshpande, James Theiler, Brian Foley, A Hollander, Samantha J Courtney, Jason Gans, E J Martinezfinley, J Mitchell, Harshini Mukundan
    Abstract:

    Viral pathogen can rapidly evolve, adapt to novel hosts and evade human immunity. The early detection of emerging viral pathogens through Biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering Biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage Biosurveillance of an entire class of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for Biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for Biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

  • selecting essential information for Biosurveillance a multi criteria decision analysis
    Online Journal of Public Health Informatics, 2014
    Co-Authors: Nicholas Generous, Kristen Margevicius, Kirsten J Taylormccabe, M G Brown, Brent W Daniel, Lauren Castro, Andrea Hengartner, Alina Deshpande
    Abstract:

    This paper proposes the use of Multi-Attribute Utility Theory to address the issue of identifying and selecting essential information for inclusion into a Biosurveillance system or process. We developed a decision support framework that can facilitate identifying data streams for use in Biosurveillance systems or processes and demonstrated utility by applying the framework to the problem of evaluating data streams for use in an global infectious disease surveillance system.

  • selecting essential information for Biosurveillance a multi criteria decision analysis
    PLOS ONE, 2014
    Co-Authors: Nicholas Generous, Kristen Margevicius, Kirsten J Taylormccabe, M G Brown, Brent W Daniel, Lauren Castro, Andrea Hengartner, Alina Deshpande
    Abstract:

    The National Strategy for Biosurveillance defines Biosurveillance as "the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels." However, the strategy does not specify how "essential information" is to be identified and integrated into the current Biosurveillance enterprise, or what the metrics qualify information as being "essential". The question of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in Biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of "essential information" for use in Biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

  • advancing a framework to enable characterization and evaluation of data streams useful for Biosurveillance
    PLOS ONE, 2014
    Co-Authors: Kristen Margevicius, Nicholas Generous, Kirsten J Taylormccabe, M G Brown, Brent W Daniel, Lauren Castro, Andrea Hengartner, Alina Deshpande
    Abstract:

    In recent years, Biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, Biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art Biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing Biosurveillance from the perspective of the fundamental data streams that have been or could be used for Biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of Biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for Biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease Biosurveillance systems, and consultation with experts in the area of Biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for Biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

Noele P Nelson - One of the best experts on this subject based on the ideXlab platform.

  • factors influencing performance of internet based Biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks
    PLOS ONE, 2014
    Co-Authors: P Barboza, Noele P Nelson, David M Hartley, Lawrence C Madoff, Jens P Linge, Abla Mawudeku, Nigel Collier, L Vaillant, Yann Le Strat, John S Brownstein
    Abstract:

    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.

  • an overview of internet Biosurveillance
    Clinical Microbiology and Infection, 2013
    Co-Authors: Noele P Nelson, David M Hartley, Ray R Arthur, Jens P Linge, Nigel Collier, P Barboza, Nigel Lightfoot, E Van Der Goot
    Abstract:

    Internet Biosurveillance utilizes unstructured data from diverse web-based sources to provide early warning and situational awareness of public health threats. The scope of source coverage ranges from local media in the vernacular to international media in widely read languages. Internet Biosurveillance is a timely modality that is available to government and public health officials, healthcare workers, and the public and private sector, serving as a real-time complementary approach to traditional indicator-based public health disease surveillance methods. Internet Biosurveillance also supports the broader activity of epidemic intelligence. This overview covers the current state of the field of Internet Biosurveillance, and provides a perspective on the future of the field.

  • event based Biosurveillance of respiratory disease in mexico 2007 2009 connection to the 2009 influenza a h1n1 pandemic
    Eurosurveillance, 2010
    Co-Authors: Noele P Nelson, John S Brownstein, David M Hartley
    Abstract:

    The emergence of the 2009 pandemic influenza A(H1N1) virus in North America and its subsequent global spread highlights the public health need for early warning of infectious disease outbreaks. Event-based Biosurveillance, based on local- and regional-level Internet media reports, is one approach to early warning as well as to situational awareness. This study analyses media reports in Mexico collected by the Argus Biosurveillance system between 1 October 2007 and 31 May 2009. Results from Mexico are compared with the United States and Canadian media reports obtained from the HealthMap system. A significant increase in reporting frequency of respiratory disease in Mexico during the 2008-9 influenza season relative to that of 2007-8 was observed (p<0.0001). The timing of events, based on media reports, suggests that respiratory disease was prevalent in parts of Mexico, and was reported as unusual, much earlier than the microbiological identification of the pandemic virus. Such observations suggest that abnormal respiratory disease frequency and severity was occurring in Mexico throughout the winter of 2008-2009, though its connection to the emergence of the 2009 pandemic influenza A(H1N1) virus remains unclear.

  • data sources for Biosurveillance
    Wiley Handbook of Science and Technology for Homeland Security, 2010
    Co-Authors: Ronald A Walters, Pete A Harlan, Noele P Nelson, David M Hartley
    Abstract:

    Biosurveillance requires a synthesis of analytic approaches derived from the natural disaster, public health, medical, meteorological, and social science communities, among others, and it is the cornerstone of early disease detection. Biosurveillance includes the analyses of timely data, often fusing time series of many different types of data, to infer the status of public health rather than solely exploiting data having diagnostic specificity. This paper summarizes major systems dedicated to such an endeavor with emphasis on those that use formal reporting sources and those that use open source collection. Although, individual systems have different missions and approaches, there are many complementarities among them that if creatively exploited could be the basis of an effective global biosuveillance enterprise. Keywords: Biosurveillance; global; alert; outbreaks; epidemic; detection; health

  • the landscape of international Biosurveillance
    Emerging Health Threats Journal 3:e3, 2010
    Co-Authors: David M Hartley, Ronald A Walters, Noele P Nelson, Lawrence C Madoff, Ray R Arthur, Roman Yangarber, Jens P Linge, Abla Mawudeku, Nigel Collier, John S Brownstein
    Abstract:

    Event-based Biosurveillance is a scientific discipline in which diverse streams of data, available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events as well as situational awareness. The Global Health Security Action Group (GHSAG) of the Global Health Security Initiative is developing a Biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these Biosurveillance systems and identifies needed future studies.

John S Brownstein - One of the best experts on this subject based on the ideXlab platform.

  • factors influencing performance of internet based Biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks
    PLOS ONE, 2014
    Co-Authors: P Barboza, Noele P Nelson, David M Hartley, Lawrence C Madoff, Jens P Linge, Abla Mawudeku, Nigel Collier, L Vaillant, Yann Le Strat, John S Brownstein
    Abstract:

    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.

  • event based Biosurveillance of respiratory disease in mexico 2007 2009 connection to the 2009 influenza a h1n1 pandemic
    Eurosurveillance, 2010
    Co-Authors: Noele P Nelson, John S Brownstein, David M Hartley
    Abstract:

    The emergence of the 2009 pandemic influenza A(H1N1) virus in North America and its subsequent global spread highlights the public health need for early warning of infectious disease outbreaks. Event-based Biosurveillance, based on local- and regional-level Internet media reports, is one approach to early warning as well as to situational awareness. This study analyses media reports in Mexico collected by the Argus Biosurveillance system between 1 October 2007 and 31 May 2009. Results from Mexico are compared with the United States and Canadian media reports obtained from the HealthMap system. A significant increase in reporting frequency of respiratory disease in Mexico during the 2008-9 influenza season relative to that of 2007-8 was observed (p<0.0001). The timing of events, based on media reports, suggests that respiratory disease was prevalent in parts of Mexico, and was reported as unusual, much earlier than the microbiological identification of the pandemic virus. Such observations suggest that abnormal respiratory disease frequency and severity was occurring in Mexico throughout the winter of 2008-2009, though its connection to the emergence of the 2009 pandemic influenza A(H1N1) virus remains unclear.

  • the landscape of international Biosurveillance
    Emerging Health Threats Journal 3:e3, 2010
    Co-Authors: David M Hartley, Ronald A Walters, Noele P Nelson, Lawrence C Madoff, Ray R Arthur, Roman Yangarber, Jens P Linge, Abla Mawudeku, Nigel Collier, John S Brownstein
    Abstract:

    Event-based Biosurveillance is a scientific discipline in which diverse streams of data, available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events as well as situational awareness. The Global Health Security Action Group (GHSAG) of the Global Health Security Initiative is developing a Biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these Biosurveillance systems and identifies needed future studies.

  • landscape of international event based Biosurveillance
    Emerging Health Threats Journal, 2010
    Co-Authors: David M Hartley, Ronald A Walters, Noele P Nelson, Lawrence C Madoff, Ray R Arthur, Roman Yangarber, Jens P Linge, Abla Mawudeku, Nigel Collier, John S Brownstein
    Abstract:

    Event-based Biosurveillance is a scientific discipline in which diverse sources of data, many of which are available from the Internet, are characterized prospectively to provide information on infectious disease events. Biosurveillance complements traditional public health surveillance to provide both early warning of infectious disease events and situational awareness. The Global Health Security Action Group of the Global Health Security Initiative is developing a Biosurveillance capability that integrates and leverages component systems from member nations. This work discusses these Biosurveillance systems and identifies needed future studies. (Published: 19 February 2010) Citation: Emerging Health Threats Journal 2010, 3 :e3. doi: 10.3134/ehtj.10.003