The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models

Advancing a Framework to Enable Characterization and Evaluation of Data Streams Useful for Biosurveillance

<div><p>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.</p></div

Ranking of Data Streamsby Biosurveillance Goal.

<p>Ranking of Data Streamsby Biosurveillance Goal.</p

Comparison of Data Stream Rankings for Early Warning Surveillance Goal.

<p>Comparison of Data Stream Rankings for Early Warning Surveillance Goal.</p

Data Stream Categories and Representative Biosurveillance Systems.

<p>Data Stream Categories and Representative Biosurveillance Systems.</p

Overview of the Biosurveillance Data Stream Framework.

<p>Overview of the Biosurveillance Data Stream Framework.</p

Biosurveillance Definitions.

<p>Biosurveillance Definitions.</p

Metric Weights if Grouped into 3 Tiers.

<p>Metric Weights if Grouped into 3 Tiers.</p

SME Non-traditional data streams binned according to the Biosurveillance Data Stream Framework.

<p>SME Non-traditional data streams binned according to the Biosurveillance Data Stream Framework.</p