Informatics

Emerging disease clusters must be detected in a timely manner so that necessary remedial action can be taken to prevent the spread of an outbreak. The Exponentially Weighted Moving Average method (EWMA) is a particularly popular method, and has been utilized for disease surveillance in the United States.

A spatio-temporal EWMA statistic is proposed for on-line disease surveillance over multiple geographic regions. To capture spatial association, disease counts of neighboring regions are pooled together, similar to a method originally proposed by Raubertas for a different control chart. Also to increase statistical power in testing multiple EWMA statistics simultaneously, false discovery rate (FDR) is used instead of the traditional family-wise error rate (FWER).

Objective

To propose a computationally simple and a fast algorithm to detect disease outbreaks in multiple regions

Champaign County is one of the largest counties in central Illinois with a population of ~207,000 and is home to the University of Illinois at Urbana-Champaign which currently has 44,500 students. In the fall the university hosts Big Ten football games which have recently been drawing an average attendance of ~45,000 people, many traveling from Chicago or other parts of the Midwest. The twin cities host a number of community events and festivals throughout the Spring and Summer. Typically the community festivals have liquor licenses whereas no alcohol is served in the football stadium. Despite the lack of alcohol availability in the stadium many fans drink during tailgate parties before and after the game.

Objective

The objective for this research project was to see if there are predictable patterns for certain annual events in Champaign County, Illinois. The focus was on how alcohol intoxication effected the population and whether or not its effects were dangerous to the community at an alarming rate.

Improving surveillance and response is a critical component of the Global Health Security Agenda. While it is impossible to predict where the next Ebola outbreak will occur, it is very likely that another outbreak will occur in the DRC. Of the 20 known outbreaks, 7 have occurred in the DRC, one as recently as 2014. To rapidly detect and respond to an Ebola outbreak, we sought to develop a real-time surveillance and response system for use in DRC and similar settings. RTI International developed Coconut Surveillance mobile software, which is currently used for real-time malaria surveillance and response in Zanzibar, Africa, where malaria elimination efforts are underway. We took this system and adapted it for Ebola as a possible tool for surveillance and response to Ebola and other (re)emerging diseases. Plans include pilot testing functionality at clinical sites in DRC, where surveillance infrastructure is limited at the local level. Coconut Surveillance is a mobile disease surveillance and rapid response system currently used for malaria elimination activities. It receives suspected positive case alerts from the field via mobile phones and uses mobile software to guide surveillance officers through a follow-up process. Coconut Surveillance runs on Android mobile devices that are used to coordinate work in the field as well as provide decision support during data collection and case management. In addition to standard case information, the GPS coordinates of the case’s household are captured as well as malaria status of all household members. Data are collected and accessed off-line, and are synchronized with a shared database when Internet connectivity is available. This tool has been used successfully in Zanzibar for more than three years and has been recognized as one of the most advanced applications of its kind.

Objective

We will describe a real-time mobile surveillance and case management system designed to organize data collected by multiple officers about cases and their contacts. We will discuss this surveillance system and its application for Ebola and other infectious diseases in the Democratic Republic of the Congo (DRC) and other similar settings. We will review the technology, results, challenges, lessons-learned, and applicability to other contexts.

Georgia Department of Public Health (DPH) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. During the 2009 H1N1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 West African Ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported Ebola disease. DPH in-house information technology (IT) staff work closely with epidemiologists to understand and accommodate surveillance needs. Through this collaboration, IT created a robust electronic surveillance and outbreak management system (OMS) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events.

Objective

To describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events.

Electronic case reporting (eCR) is defined as the fully or semiautomated generation and electronic transmission of reportable disease case reports from an electronic health record (EHR) system to public health authorities, replacing the historically paper-based process. ECR has been reported to increase the number, accuracy, completeness and timeliness of surveillance case reports. Chicago Department of Public Health (CDPH) collaborated with Alliance of Chicago (AOC) to develop an application to generate electronic provider reports (ePR) for chlamydia (CT) and gonorrhea (GC) cases from the EHR system managed by AOC and send ePR records to the Illinois National Electronic Disease Surveillance System (I-NEDSS). This application was tested in the EHR database of Health Center A in AOC’s network. It is essential to ensure ePR data are accurate, so that public health receives correct information to take actions if needed. Therefore, evaluation is needed to assess ePR records data quality.

Objective

To describe the evaluation process to assess data quality during development of an electronic case report application, and to describe the evaluation results

EpiCore draws on the knowledge of a global community of human, animal, and environmental health professionals to verify information on disease outbreaks in their geographic regions. By using innovative surveillance techniques and crowdsourcing these experts, EpiCore enables faster global outbreak detection, verification, and reporting

Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem (BSVE). While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and 10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accesible.

Objective

LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications.

Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing electronic case reporting (eCR) of sexually transmitted infections (STI) from a clinical partner.

Objective

We reviewed CCDs (a type of consolidated clinical data architecture (C-CDA) document) shared by our clinical partner, Planned Parenthood of the Great Northwest and Hawaiian Islands (PPGNHI) since October, 2015. Analyses focuses on:

-Completeness

-Degree to which the CCD matches program area information needs

-Differences in EHR generation methods

-Presence and location of triggers (based on the Reportable Conditions Trigger Codes) that would initiate CCD generation.

As syndromic surveillance systems continue to grow, new opportunities have arisen to utilize the data in new or alternative ways for which the system was not initially designed. For example, in many jurisdictions syndromic surveillance has recently become population-based, with 100% coverage of targeted emergency department encounters. This makes the data more valuable for real- time evaluation of public health and prevention programs. There has also been increasing pressure to make more data publicly available – to the media, academic partners, and the general public. 

Objective

This roundtable will provide a forum for national, state, and local managers of syndromic surveillance systems to discuss how they identify, monitor, and respond to changes in the nature of their data. Additionally, this session will focus on the strengths and weakness of the syndromic surveillance systems for supporting program evaluation and trend analysis. This session will also provide a forum where subject matter experts can discuss the ways in which this deep understanding of their data can be leveraged to forge and improve partnerships with academic partners.