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data stream

Description

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 leaves unanswered how 'essential information' is to be identified and integrated, or what the metrics qualify information as being 'essential'. Multi-Attribute Utility Theory (MAUT), a type of multi-criteria decision analysis, provides a structured approach that can offer solutions to this problem. While the use of MAUT has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance. We have developed a decision support analytic framework using MAUT that can facilitate identifying data streams for use in biosurveillance. We applied this framework to the problem of evaluating data streams for use in a global infectious disease surveillance system.

Objective

To describe how multi-criteria decision analysis can be applied to identifying essential biosurveillance information and demonstrate feasibility by applying it to prioritize data streams.

Submitted by elamb on
Description

Multiple data sources are used in a variety of biosurveillance systems. With the advent of new technologies, globalization, high performance computing, and "big data" opportunities, there are seemingly unlimited potential data streams that could be useful in biosurveillance. Data streams have not been universally defined in either the literature or by specific biosurveillance systems. The definitions and framework that we have developed enable a characterization methodology that facilitates understanding of data streams and can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities- filling a gap recognized in both the public health and biosurveillance communities.

Objective

To develop a data stream-centric framework that can be used to systematically categorize data streams useful for biosurveillance systems, supporting comparative analysis

Submitted by knowledge_repo… on
Description

Local, national, and global infectious disease surveillance systems have been implemented to meet the demands of monitoring, detecting, and reporting disease outbreaks and prevalence. Varying surveillance goals and geographic reach have led to multiple and disparate systems, each using unique combinations of data streams to meet surveillance criteria. In order to assess the utility and effectiveness of different data streams for global disease surveillance, a comprehensive survey of current human, animal, plant, and marine surveillance systems and data streams was undertaken. Information regarding surveillance systems and data streams has been (and continues to be) systematically culled from websites, peer-reviewed literature, government documents, and subject-matter expert consultations.

Objective:

The goal of this project is to identify systems and data streams relevant for infectious disease biosurveillance. This effort is part of a larger project evaluating existing and potential data streams for use in local, national, and international infectious disease surveillance systems with the intent of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.

 

Submitted by Magou on
Description

Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods [1,2]. Spatial Scan Statistics have been adapted to analyze multivariate data sources [1]. In this context, only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. A multi-objective scan was proposed to detect clusters for a single data source [3].

Objective:

To incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection using statistical tools from multi-criteria analysis.

Submitted by Magou on
Description

Los Alamos National Laboratory has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system. We used a novel method of evaluating the effectiveness of data streams called the “surveillance window”. The concept of the surveillance window is defined as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. We used a stepwise approach to defining disease specific surveillance windows;

  1. Timeline generation through historical perspectives and epidemiological simulations.
  2. Identifying the surveillance windows between changes in “epidemiological state” of an outbreak.
  3. Data streams that are used or could have been used due to their availability during the generated timeline are identified. If these data streams fall within a surveillance window, and provide both actionable and non-actionable information, they are deemed to have utility.

 

Objective

The goal of this project is the evaluation of data stream utility in integrated, global disease surveillance. This effort is part of a larger project with the goal of developing tools to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.

Submitted by hparton on
Description

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Objective:

Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts.

 

Submitted by Magou on
Description

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) has been funded by the Defense Threat Reduction Agency to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. Specifically, this project entails the evaluation of data streams either currently in use in surveillance systems or new data streams having the potential to enable early disease detection. An overview of this project will be presented, together with results of data stream evaluation. This project will help gain an understanding of data streams relevant to early warning/monitoring of disease outbreaks.

Objective:

The overall objective of this project is to provide a robust evaluation of data streams that can be leveraged from existing and developing national and international disease surveillance systems, to create a global disease monitoring system and provide decision makers with timely information to prepare for and mitigate the spread of disease.

Submitted by Magou on
Description

The evaluation of biosurveillance system components is a complex, multi-objective decision that requires consideration of a variety of factors. Multi-Criteria Decision Analysis provides a methodology to assist in the objective analysis of these types of evaluation by creating a mathematical model that can simulate decisions. This model can utilize many types of data, both quantitative and qualitative, that can accurately describe components. The decision-maker can use this model to determine which of the system components best accomplish the goals being evaluated. Before MCDA can be utilized effectively, an evaluation framework needs to be developed. We built a robust framework that identified unique metrics, surveillance goals, and priorities for metrics. Using this framework, we were able to use MCDA to assist in the evaluation of data streams and to determine which types would be of most use within a global biosurveillance system.

Objective:

The use of Multi-Criteria Decision Analysis (MCDA) has traditionally been limited to the field of operations research, however many of the tools and methods developed for MCDA can also be applied to biosurveillance. Our project demonstrates the utility of MCDA for this purpose by applying it to the evaluation of data streams for use in an integrated, global biosurveillance system.

 

Submitted by Magou on