Deshpande Alina

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. In this presentation, we will describe a suite of tools developed at Los Alamos National Laboratory (LANL) that provide actionable information and knowledge for enhanced situational awareness during an unfolding event; The biosurveillance resource directory (BRD), the biosurveillance analytics resource directory (BaRD) and the surveillance window app (SWAP).

Objective

To develop a suite of tools that provides actionable information and knowledge for enhanced situational awareness during an unfolding event such as an infectious disease outbreak.

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.

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. By placing a frame of reference for where a case count is during an outbreak (in the early, peak, or late stages) and indicating whether the unfolding events are still within a surveillance window that would allow for feasible control, the app provides enhanced situational awareness of a decision maker. This tool therefore increases the granularity of situational awareness available to any user in the global biosurveillance community.

Objective

The goal of this project is to develop a cross platform app that contextualizes incoming information during an infectious disease outbreak based on historical data. The app makes use of a surveillance window concept in order to support decision making. This effort is part of a larger project with the goal of developing reference tools and analytics to provide decision-makers with timely information to predict, prepare for, and mitigate the spread of disease.

Los Alamos National Laboratory (LANL) was tasked with developing methods 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'. We defined a surveillance window as the brief period of time when information gathered can be used to assist decision makers in effectively responding to an impending outbreak. Information obtained for data streams beyond this window is deemed to have limited use.

Objective

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

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

Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.

Objective:

Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.

Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.

Objective:

Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.