Biosurveillance Data Stream Framework: A Novel Approach to Characterization and Evaluation

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.

August 22, 2018

Enhancing Situational Awareness by Ground Truthing with Historical Outbreaks

Los Alamos National Laboratory (LANL) has been funded by the Defense Threat Reduction Agency to develop tools that enhance situational awareness in infectious disease surveillance. We have applied the concept of the surveillance window to the development of a cross platform app (SWAP). This app allows the user to place information on case counts or disease occurrence in a specific location within the context of a historical outbreak curve to help determine whether prevention or mitigation action should be taken.

August 22, 2018

Tools and Apps to Enhance Situational Awareness for Global Disease Surveillance

Situational awareness is important for both early warning and early detection of a disease outbreak, and analytics and tools that furnish information on how an infectious outbreak would either emerge or unfold provide enhanced situational awareness for decision makers/analysts/public health officials, and support planning for prevention or mitigation. Data sharing and expert analysis of incoming information are key to enhancing situational awareness of an unfolding event.

May 02, 2019

Evaluating Biosurveillance System Components using Multi-Criteria Decision Analysis

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.

March 19, 2018

A Systematic Evaluation of Data Streams for Global Disease Surveillance

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.

June 12, 2018

The Biosurveillance Resource Directory - A One-Stop Shop for Systems, Sources, and Tools

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.

July 10, 2018

Parametric Uncertainty in Intra-Herd Foot-and-Mouth Disease Epidemiological Models

Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak.

July 09, 2018

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