Data Analytics

The National Syndromic Surveillance Program's (NSSP) instance of ESSENCE* in the BioSense Platform generates about 35,000 statistical alerts each week. Local ESSENCE instances can generate as many as 5,000 statistical alerts each week. While some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. By design, statistical alerts should indicate potential clusters that warrant a syndromic surveillance practitioner's time and focus. However, practitioners frequently ignore statistical alerts altogether because of the overwhelming volume of data and alerts. In 2008, staff in the Virginia Department of Health experimented with rules that could be used to rank the statistical output generated in ESSENCE alert lists. Results were shared with Johns Hopkins University Applied Physics Lab (JHU/APL), the developer of ESSENCE, and were early inputs into what is now known as "myAlerts," an ESSENCE function that syndromic surveillance practitioners can use to customize alerting and sort through statistical noise. NSSP ESSENCE produces a shared alert list by syndrome, county, and age-group strata, which generates an unwieldy but rich data set that can be studied to learn more about the importance of these statistical alerts. Ultimately, guidance can be developed to help syndromic surveillance practitioners set up meaningful ESSENCE myAlerts effective in identifying clusters with public health importance.

Objective: Find practical ways to sort through statistical noise in syndromic data and make use of alerts most likely to have public health importance.

In recent years, studies in health and medicine have shifted toward eHealth communication and the relationships among human interaction, computer literacy, and digital text content in medical discourses (1-6). Clinicians, however, continue to struggle with EHR usability, including how to effectively capture patient data without error (7-9). Usability is especially problematic for clinicians, who must now acquire new skills in electronic documentation (10). Challenges with the EHR occur because of clinicians'™ struggle with attention to the non-linear format of clinical content and automated technologies (11). It is therefore important to understand how attention structures are visually situated within the EHR's narrative architecture and audience for whom electronic text is written. It is equally important to visualize and track how automated language and design in health information technology (HIT) affect users' attention when documenting clinical narratives (12). In the study of health information technology, researchers of eHealth platforms need to recognize how the construction of human communication lies within the metaphoric expression, design, and delivery of the EHR's information architecture (13). Many studies of electronic health records (EHR) examine the design and usability in the development stages. Some studies focus on the economic value of the EHR Medicare incentive program, which affects providers' return on investment (ROI). Few studies, however, identify the communicative value of how attention structures within the EHR'™s information architecture compete for users' attention during the clinical documentation process (9, 14).

Objective: To track and visually assess how automated attention structures within the electronic health record (EHR) compete for clinicians attention during computer physician order entry that could potentially lead to transactions hazards in the clinical narrative.

Hand, foot, and mouth disease is a highly infectious disease common among early childhood populations caused by human enteroviruses (Enterovirus genus).1 The enteroviruses responsible for HFMD generally cause mild illness among children in the United States with symptoms of fever and rash/blisters, but have also been linked to small outbreaks of severe neurological disease such as meningitis, encephalitis, and acute flaccid myelitis.2 Enteroviruses circulate year-round but increase in the summer-fall months across much of the United States.3 The drivers of this seasonality are not fully understood, but research indicates climatic factors, rather than demographic ones, are most likely to drive the amplitude and timing of the seasonal peaks.3 A recent CDC study on nonpolio enteroviruses identified dew point temperature as a strong predictor of local enterovirus seasonality, explaining around 30% of the variation in intensity of transmission across the United States.3

Objective: To assess the relationship between seasonal increases in emergency department (ED) and urgent care center (UCC) visits for hand, foot, and mouth disease (HFMD) among children 0-4 years old and average dew point temperatures in Virginia. To determine if this relationship can be used to develop an early warning tool for high intensity seasons of HFMD, allowing for earlier targeted public health action and communication to the community and local childcare centers during these high intensity seasons.

The use of syndromic surveillance systems has evolved over the last decade, and increasingly includes both infectious and non- infectious topic areas. Public health agencies at the national, state, and local levels often need to rapidly develop new syndromic categories, or improve upon existing categories, to enhance their public health surveillance efforts. Documenting this development process can help support increased understanding and user acceptance of syndromic surveillance. This presentation will highlight the visualization process being used by CDC’s National Syndromic Surveillance Program (NSSP) program to develop and refine definitions for syndromes of interest to public health programs.

Objective: To describe the use of uni-grams, bi-grams, and tri-grams relationships in the development of syndromic categories.

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.

Overweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence. With the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention. Nearly three- quarters of Americans go online daily6, for functions like connecting with individuals via social network sites7. Like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes8. Previous studies examining social networking sites typically examine a few selected social networking sites (example studies9,10), although individuals could be members of multiple social networking sites. To better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight management community members' other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments.

Objective: We aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called Reddit.

Infectious disease was the second most common cause of death in 1949, and the epidemic situation of infectious diseases was so severe that the Chinese government made major investments to the control and prevention of infectious diseases. During the past 60 years the development of the notifiable disease surveillance system in China has experienced 3 phases, including germination stage, development stage, improvement and consolidation stage (1). As the quality of infectious diseases surveillance has been improved stepwisely, the national morbidity of class A and B notifiable disease decreased from 7157.5 per 100,000 in 1970 to 225.8 per 100,000 in 2013, and the mortality decreased from 56.0 per 100,000 in 1959 to 1.2 per 100,000 in 2013(2).

Objective: We aimed to review the development and changes of National Notifiable Disease Surveillance System (NNDSS) from 1950 to 2013, and to analyze and summarize the changes in regulations and public health surveillance practices in China.

Within the traditional surveillance of notifiable infectious diseases in Germany, not only are individual cases reported to the Robert Koch Institute, but also outbreaks themselves are recorded: A label is assigned by epidemiologists to each case, indicating whether it is part of an outbreak and of which. This expert knowledge represents, in the language of machine leaning, a "ground truth" for the algorithmic task of detecting outbreaks from a stream of surveillance data. The integration of this kind of information in the design and evaluation of algorithms is called supervised learning.

Objective: By systematically scoring algorithms and integrating outbreak data through statistical learning, evaluate and improve the performance of automated infectious-disease-outbreak detection. The improvements should be directly relevant to the epidemiological practice. A broader objective is to explore the usefulness of machine-learning approaches in epidemiology.

The motivation for this project is to provide greater situational awareness to DoD epidemiologists monitoring the health of military personnel and their dependents. An increasing number of data sources of varying clinical specificity and timeliness are available to the staff. The challenge is to integrate all the information for a coherent, up-to-date view of population health. Developers at the Johns Hopkins Applied Physics Laboratory, in collaboration with medical epidemiologists at the Armed Forces Health Surveillance Branch, previously designed a multivariate decision support tool to add to the DoD implementation of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE). Data sources included clinical encounter records including free-text chief complaints, filled prescription records, and laboratory test orders and results. Filtered data streams were derived from these sources for daily monitoring, and alerting algorithms were customized and applied to the resulting time series. We built BNs to derive overall levels of concern from the collection of data streams and algorithm outputs to derive, in the form of daily fusion alerts, the overall level of various outbreak concerns. Visualizations made apparent which data features accounted for these concerns, including drill-down to the level of patient record details. Advantages of the BN approach are this transparency and the capacity for assessments using incomplete data and incorporating novel and report-based data streams. The need for such fusion was nearly unanimous in a global survey of public health epidemiologists [1]. Our proof-of-concept system based on commercial BN software was well received by a cross-section of DoD health monitors. The new software tools we apply in this project use freely available R packages which provide more comprehensive tools for BN training and development. These results will allow us to improve the analytic fusion abilities of DoD ESSENCE, as well as in civilian surveillance systems Our testing procedures and results are presented below.

Objective: Our project goal is to enhance the capability of automating health surveillance[MOU1] by US Department of Defense (DoD) epidemiologists. We employ software tools that build and train Bayesian networks (BNs) to facilitate the development of analytic fusion of multiple, disparate data sources comprising both syndromic and diagnostic data streams for rapid estimation of overall levels of concern for potential disease outbreaks. Working with previously developed heuristic BNs, we evaluate the ability of machine learning algorithms to detect outbreaks with greater accuracy. We use historical training data on the ability to detect outbreaks of influenza-like illness (ILI).

Highly pathogenic avian influenza (HPAI) subtype H5N1 virus causes a highly contagious disease in poultry with up to 100% mortality and occasionally causes sporadic human infection. The first outbreak of HPAI H5N1 in Africa was reported in Nigeria in 2006 and has since been reported in seven other African countries with confirmed human cases and outbreaks in poultry. Since the emergence of Highly Pathogenic Avian Influenza (HPAI), virus subtype H5N1 in Ghana in 2007, outbreaks in poultry have led to dire economic consequences for the poultry sector, resulting from mass destruction of affected flocks. An economy heavily dependent on agriculture, the persistence of outbreaks threaten the livelihood of farmers who depend on poultry production for survival. Despite significant efforts made in HPAI-H5N1 control and prevention in Ghana, outbreaks persist and continue to spread to new areas. It is uncertain to what extent different pathways contribute to the introduction and the dissemination of the virus in Ghana. There is a need to understand the complex nature of the interactions between local and migratory fowl, the risk of transmission due to human endeavor and trade mechanisms that increase the likelihood of HPAI-H5N1 outbreaks in Ghana.

Objective: The purpose of the study was to characterize the spatial distribution and temporal patterns of laboratory confirmed H5N1 outbreaks from January 2007 to December 2017 in Ghana.