BioSurveillance

Dogs, cats and other companion animals have played an integral role in many aspects of human life. Human and companion animal (CAs) interactions have a wide range of benefits to human health [1-3]. The threat of zoonotic transmission between CAs and humans is exacerbated by proximity (56% of dog owners and 62% of cat owners sleep with their animal next to them [4]) and the number of diseases CAs share with humans. Many of these highlighted zoonoses are spread by direct contact, and others are vector-transmitted (e.g., fleas, ticks, flies, and mosquitos). Within the realm of the One-Health concept, CAs can serve multiple roles in zoonotic transmission chains between humans and animals. They can serve as intermediate hosts between wildlife reservoirs and humans, or as possible sentinel or proxy species for emerging diseases [5]. Given the large number of CAs within the United States (estimated 72 million pet dogs, 81 million pet cats), understanding and preventing the diseases prevalent in CA populations is of utmost importance. Biosurveillance is a critical component of One Health initiatives including zoonotic disease mitigation and control. As Lead Service for Veterinary Animal and Public Health Services, the Army has a responsibility to champion biosurveillance efforts to support One Health initiatives, improving Servicemember, family, and retiree health across the Joint Force. Additionally, with military personnel experiencing apparent increased rates of job-reducing ailments such as diarrheal, bacterial and viral disease [6- 8], it is essential that the Army focus on maximizing their operational potential by minimizing the amount of time personnel are sick from these transmissible diseases and observing potential sources of infection. By observing the zoonotic disease burden in privately owned (POAs) and government-owned (GOAs) animals, public health investigators can increase focus on what transmittable diseases are at greatest risk of being spread from companion animals to military personnel. To address this potential source of infection, the Department of Defense (DoD) sought and continues to seek to establish a centralized and integrated veterinary zoonotic surveillance system to provide Commanders with a clear picture of disease burden [9]. With this assigned responsibility, the Army Veterinary Service (VS) seeks to centralize and enhance surveillance efforts through the Remote Online Veterinary Record (ROVR) Electronic Health Record (EHR), an enterprise web-based application to support the Army VS, accurately establishing a zoonotic epidemiological baseline and sustaining consistent future reporting.

Objective: We assesed the feasibility of a zoonotic disease surveillance system through the current EHR (ROVR) for all POAs and GOAs. Additionally, we conducted a retrospective observational study querying and collecting reported zoonoses of interest, for 2017.

After the 2009 H1N1 pandemic, the Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense indicated œbiodefense would include emerging infectious disease. In response, DTRA launched an initiative for an innovative, rapidly emerging capability to enable real-time biosurveillance for early warning and course of action analysis. Through competitive prototyping, DTRA selected Digital Infuzion to develop the platform and next generation analytics. This work was extended to enhance collaboration capabilities and to harness data science and advanced analytics for multi-disciplinary surveillance including climate, crop, and animal as well as human data. New analysis tools ensure the BSVE supports a One Health paradigm to best inform public health action. Digital Infuzion and DTRA first introduced the BSVE to the ISDS community at the 2013 annual conference SWAP Meet. Digital Infuzion is pleased to present the mature platform to this community again as it is now a fully developed capability undergoing FedRAMP certification with the Department of Homeland Security's National Biosurveillance Integration Center and Is the basis for Digital Infuzion's HARBINGER ecosystem for biosurveillance.

Objective: While there is a growing torrent of data that disease surveillance could leverage, few effective tools exist to help public health professionals make sense of this data or that provide secure work-sharing and communication. Meanwhile, our ever more-connected world provides an increasingly receptive environment for diseases to emerge and spread rapidly making early warning and collaborative decision-making essential to saving lives and reducing the impact of outbreaks. Digital Infuzion's previous work on the Defense Threat Reduction Agency (DTRA)'s Biosurveillance Ecosystem (BSVE) built a cloud-based platform to ingest big data with analytics to provide users a robust surveillance environment. We next enhanced the BSVE data sources and analytics to support an integrated One Health paradigm. The resulting BSVE and Digital Infuzion's HARBINGER platform include: 1) identifying and ingesting data sources that span global human, animal and crop health; 2) inclusion of non-health data such as travel, weather, and infrastructure; 3) the data science tools, analytics and visualizations to make these data useful and 4) a fully-featured Collaboration Center for secure work-sharing and communication across agencies.

In 2012 - 2017 in Azerbaijan there was an unexpected increase of abortions in cattle and sheep that was unrelated to brucellosis or chlamydia infection. The first confirmed case of Schmallenberg disease was received from Beylagan district of Azerbaijan in October 2012. The import of cattle from Europe to Azerbaijan has commenced in 2012. Therefore, the surveillance study was launched to determine spread of infection among cattle and sheep and to monitor the situation in the country.

Objective: Schmallenberg virus (SBV) is an orthobunyavirus that primarily infects domestic and wild ruminants and causes symptoms such as transient fever, diarrhea, reduced milk production, congenital malformations and abortion. The first virus was identified in 2011 at the onset of a major outbreak in Europe (Germany, Hungary, and France).

Current biosurveillance systems run multiple univariate statistical process control (SPC) charts to detect increases in multiple data streams. The method of using multiple univariate SPC charts is easy to implement and easy to interpret. By examining alarms from each control chart, it is easy to identify which data stream is causing the alarm. However, testing multiple data streams simultaneously can lead to multiple testing problems that inflate the combined false alarm probability. Although methods such as the Bonferroni correction can be applied to address the multiple testing problem by lowering the false alarm probability in each control chart, these approaches can be extremely conservative. Biosurveillance systems often make use of variations of popular univariate SPC charts such as the Shewart Chart, the cumulative sum chart (CUSUM), and the exponentially weighted moving average chart (EWMA). In these control charts an alarm is signaled when the charting statistic exceeds a pre-defined control limit. With the standard SPC charts, the false alarm rate is specified using the in-control average run length (ARL0). If multiple charts are used, the resulting multiple testing problem is often addressed using family-wise error rate (FWER) based methods that are known to be conservative - for error control. A new temporal method is proposed for early event detection in multiple data streams. The proposed method uses p-values instead of the control limits that are commonly used with standard SPC charts. In addition, the proposed method uses false discovery rate (FDR) for error control over the standard ARL0 used with conventional SPC charts. With the use of FDR for error control, the proposed method makes use of more powerful and up-to-date procedures for handling the multiple testing problem than FWER-based methods.

Objective: To propose a computationally simple, fast, and reliable temporal method for early event detection in multiple data streams.

There is a significant body of literature on the use of social media for monitoring ailments such as influenza-like illness1 and cholera,2 as well as public opinions on topics such as vaccination.3 In general, these studies have shown that social media correlates well with official data sources,1,2,3 with the trends identifiable before official data are available.2 However, less is known about the impact of integrating social media into public health practice, and resulting interventions. Therefore, the ISDS Social Media for Disease Surveillance Workgroup initiated a systematic literature review on the use of social media for actionable biosurveillance.

Objective

The objective of this study is to systematically review the literature on the use of social media for biosurveillance in order to evaluate whether this data source can improve public health practice or community health outcomes.

The U.S. Defense Threat Reduction Agency (DTRA) is funding multiple development efforts directed at enhanced platforms to support bio-surveillance analysts under their Bio-surveillance Ecosystem (BSVE) program. These efforts include well-integrated user interface systems and advanced algorithmic concepts to facilitate analysis of diverse, pertinent data sources including traditional bio-surveillance data sources as well as social media inputs. A central challenge in this development effort is a practical, effective, method to test these prototype systems. This presentation discusses a simulation-based testbed to allow quantitative evaluation of analytical methods through controlled injection of simulated outbreak-related information into test data streams.

Objective:

To develop a software toolset to serve as a flexible test environment for bio-surveillance systems by injecting controlled, simulation-based, data modifications into a variety of traditional and non-traditional bio-surveillance sources.

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.