BioSurveillance

Life science and biotechnology advances have provided transforming capabilities that could be leveraged for integrative global biosurveillance. Global infectious disease surveillance holds great promise as a tool to mitigate the endemic and pandemic infectious disease impacts, and remains an area of broad international interest. All nations have significant needs for addressing infectious diseases that impact human health and agriculture, and concerns for bioenergy research and environmental protection. In January 2011, Los Alamos National Laboratory, Department of State, and the Defense Threat Reduction Agency co-hosted the "Global Biosurveillance Enabling Science and Technology" Conference. Guided by the National Strategy for Countering Biological Threats, and joined by major government stakeholders, the primary objective was to bring together the international technical community to discuss the scientific basis and technical approaches to an effective and sustainable InGBSV system and develop a research agenda enabling a long-term, sustainable capability. The overall objective of the conference was to develop a technology road map for InGBSV, with three underlying components: 1) Identify opportunities for integrating existing biosurveillance systems, the near-term technological advancements that can support such integration, and the priority of future research and development areas; 2) Identify the required technical infrastructure to support InGBSV, such as methodologies and standards for technology evaluation, validation and transition; 3) Identify opportunities, and the challenges that must be overcome, for partnerships and collaborations.

Objective

To review observations and conclusions from a recent Global Biosurveillance conference, provide an assessment of the scientific and technical capabilities and gaps to achieve an effective and sustainable integrative global biosurveillance (InGBSV) system, and recommend research and development priorities enabling InGBSV.

Argus is an event-based, multi-lingual surveillance system which captures and analyzes information from publicly available Internet media. Argus produces reports that summarize and contextualize indications and warning (I&W) of emerging threats, and makes these reports available to the system's users. The significance of the Escherichia coli (EHEC) outbreak analyzed here lies primarily in the fact that it raised epidemiological questions and public health infrastructure concerns that have yet to be resolved, and required the development of new resources for detecting and responding to newly-emerging epidemics.

Objective

To demonstrate how event-based biosurveillance, using direct and indirect I&W of disease, provides early warning and situational awareness of the emergence of infectious diseases that have the potential to cause social disruption and negatively impact public health infrastructure, trade, and the economy. Specifically, tracking of I&W during the 2011 enterohaemorrhagic EHEC O104:H4 outbreak in Germany and Europe was selected to illustrate this methodology.

Event-based biosurveillance is a practice of monitoring diverse information sources for the detection of events pertaining to human, plant, and animal health. Online documents, such as news articles, newsletters, and (micro-) blog entries, are primary information sources in it. Document classification is an important step to filter information and machine learning methods have been successfully applied to this task.

Objective

The objective of this literature review is to identify current challenges in document classification for event-based biosurveillance and consider the necessary efforts and the research opportunity.

With the proliferation of social networks, the web has become a warehouse of patient discussions and reports, estimated at 10 billion records and growing at a rate of 40 percent per year. First Life Research, Ltd. (FLR), has searched and mapped thousands of these discussions and indexed hundreds of millions of reports (currently 960M) and is engaged in building web-based solutions that enable the public and public health practitioners to access massive health-related information and knowledge generated from the crowd.

Objective

With a large population sharing experiences regarding health issues and treatments online via social media platforms, generating novel data sets composed of massive unstructured user-generated content of health reports. This collective intelligence is referred to as the ‘Wisdom of the crowd’. This is a brief overview of data research engaging this unique statistical sample referred to as the ‘Crowd trial’ as an innovative element in health monitoring, enabling early detection and intervention by health professionals, regulators and pharmaceutical companies.

INDICATOR provides an open source platform for biosurveillance and outbreak detection. Data sources currently include emergency department, patient advisory nurse, outpatient clinic, and school absence activity We are currently working with the University of Illinois College of Veterinary Medicine and will include veterinary data so that animal and human health data can be analyzed together.

Objective

INDICATOR, an existing biosurveillance system, required an updated user interface to support more data sources and more robust reporting and data visualization.

Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an anomalous subgraph which might be indicative of an emerging event. Typically, however, the network structure is unknown, and must be learned from unlabeled data, given only the time series of observed counts (e.g. daily hospital visits for each zip code).

Objective

Our goal is to learn the underlying network structure along which a disease outbreak might spread, and use the learned network to improve the timeliness and accuracy of outbreak detection.

In the last decade, the scope of public health (PH) surveillance has grown, and biosurveillance capacity has expanded in Duval County. In 2004, the Duval County Health Department (DCHD) implemented a standalone syndromic surveillance (SS) system which required the manual classification and entry of emergency department (ED) chief complaints by hospital staff. At that time, this system, in conjunction with other external systems (e.g. CDC ILInet, FluStar, NRDM) were used to conduct surveillance for health events. Recommendations from a 2007 ISDS panel were used to strengthen surveillance within Duval County. Later that year, the Florida DOH moved to a statewide SS system and implemented ESSENCE which has been expanded to include 1) ED record data from 176 hospitals (8 within Duval County); 2) Reportable disease case records from Merlin; 3) Florida Poison Information Network consultations; and, 4) Florida Office of Vital Statistics death records (1). ESSENCE has subsequently become a platform for rapid data analysis, mapping, and visualization across several data sources (1). As a result, ESSENCE has improved business processes within DCHD well beyond the initial scope of event detection. These improvements have included 1) expansion of the ability to create visualizations (e.g. epi-curves, charts, and maps); 2) reduction in the time required to produce reports (e.g. newsletters, media responses); 3) reduction in staff training needs; and 4) augmentation of epidemiology processes (e.g. active case finding, emergency response, quality improvement (QI)), and closing the PH surveillance loop.

Objective

This paper reviews the evolution of biosurveillance in Duval County, FL and characterizes the subsequent improved execution of epidemiology functions as a result of the implementation of the Early Notification of Community-based Epidemics (ESSENCE) system.

Influenza is a recurrent viral disease with potential to result in pandemics. Therefore it is necessary to have a timely, responsive and accurate detection. The use of Twitter as a source for data mining in biosurveillance has been previously shown useful, and it also has a potential for real-time visualization. However these efforts target messages in English, omitting from surveillance the part of users that speaks other languages, such as Spanish.

Objective

Identify the potential of Twitter as a source for monitoring and visualizing content regarding Influenza-like-Illness in Spanish-speaking populations for biosurveillance purposes.

The purpose of the National Collaborative for Bio-preparedness (NCB-P) is to enhance biosurveillance and situational awareness to better inform decision-making using a statewide approach. EMS represents a unique potential data source because it intersects with patients at the point of insult or injury, thus providing information on the timing and location of care. North Carolina uses a standardized EMS data collection system, the Prehospital Medical Information System (PreMIS), to collect information on EMS encounters across the state using the National EMS Information System (NEMSIS) template. Since NEMSIS is planned to be incorporated by EMS agencies in every state, an EMS-based approach to biosurveillance is extensible nationally.

Objective

To develop a statewide biosurveillance system based on emergency medical services (EMS) information which employs both symptom-based illness categorization and spatiotemporal analysis.

Particularly in resource-poor settings, syndromic surveillance has been proposed as a feasible solution to the challenges in meeting the new disease surveillance requirements included in the World Health Organization's International Health Regulations (2005).

Objective

The aim of this study is to demonstrate how syndromic surveillance systems are working in low-resource settings while identifying the key best practices and considerations.