Loschen Wayne

The ESSENCE application supports users' interactive analysis of data by clicking through menus in a user interface (UI), and provides multiple types of user defined data visualization options, including various charts and graphs, tables of statistical alerts, table builder functionality, spatial mapping, and report generation. However, no UI supports all potential analysis and visualization requirements. Rapidly accessing data processed through ESSENCE using existing access control mechanisms, but de-coupled from the UI, supports innovative analyses, visualizations and reporting of these data using other tools.

Objective: To describe and provide examples of the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) application programming interface (API) as a part of disease surveillance workflows.

With the increase in the amount of public health data along with the growth of public health informatics, it is important for epidemiologists to understand the current trends in technology and the impact they may have in the field. Because it is unfeasible for public health professionals to be an expert in every emerging technology, this presentation seeks to provide them with a better understanding of how emerging technologies may impact the field and the level of expertise required to realize benefits from the new technologies. Furthermore, understanding the capabilities provided by emerging technologies may guide future training and continuing education for public health professionals.

Objective: The objective of this presentation is to explore emerging technologies and how they will impact the public health field. New technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) will likely be incorporated into epidemiological methods and processes. This presentation will provide an overview of these technologies and focus on how they may impact public health surveillance in the future.

The ESSENCE system is a community-driven disease surveillance system. Installed in over 25 jurisdictions across the US, the system is built on a single codebase that is shared across all instances. While each individual location can customize many of the settings, data sources, and configurations, the underlying code and functionality is shared. This means that when one jurisdiction works with the Johns Hopkins University Applied Physics Laboratory (JHU/APL) to create a new feature, it is available to all sites.

Objective: The objective of this presentation is to discuss the new features that are under development for ESSENCE in 2019. This is a chance to describe the features, the use cases for the features, and open a dialogue with the community on potential new enhancements that are available.

Unlike other health threats of recent concern for which widespread mortality was hypothetical, the high fatality burden of opioid overdose crisis is present, steadily growing, and affecting young and old, rural and urban, military and civilian subpopulations. While the background of many public health monitors is mainly infectious disease surveillance, these epidemiologists seek to collaborate with behavioral health and injury prevention programs and with law enforcement and emergency medical services to combat the opioid crisis. Recent efforts have produced key terms and phrases in available data sources and numerous user-friendly dashboards allowing inspection of hundreds of plots. The current effort seeks to distill and present combined fusion alerts of greatest concern from numerous stratified data outputs. Near-term plans are to implement best-performing fusion methods as an ESSENCE module for the benefit of OHA staff and other user groups.

Objective: In a partnership between the Public Health Division of the Oregon Health Authority (OHA) and the Johns Hopkins Applied Physics Laboratory (APL), our objective was develop an analytic fusion tool using streaming data and report-based evidence to improve the targeting and timing of evidence-based interventions in the ongoing opioid overdose epidemic. The tool is intended to enable practical situational awareness in the ESSENCE biosurveillance system to target response programs at the county and state levels. Threats to be monitored include emerging events and gradual trends of overdoses in three categories: all prescription and illicit opioids, heroin, and especially high-mortality synthetic drugs such as fentanyl and its analogues. Traditional sources included emergency department (ED) visits and emergency management services (EMS) call records. Novel sources included poison center calls, death records, and report-based information such as bad batch warnings on social media. Using available data and requirements analyses thus far, we applied and compared Bayesian networks, decision trees, and other machine learning approaches to derive robust tools to reveal emerging overdose threats and identify at-risk subpopulations.