APL makes strides in public health

For over 15 years, the Johns Hopkins Applied Physics Laboratory (APL) has developed technologies to assist in the study of public health in order to detect and prevent the spread of disease. These technologies are designed to be easily distributable and open source, meaning that anyone can look at their design.

APL’s involvement in public health research began in 1998 with the beginning of development of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). The development began after APL researcher Joe Lombardo received grants from the U.S. Department of Defense (DoD) and the National Capital Region (NCR). NCR is made up of a collection of Maryland and Virginia counties and Washington, D.C., which pool resources for the purpose of homeland security.

The project was completed after the September 2001 anthrax scare, in which letters containing anthrax spores were mailed to media officials and two U.S. Senators. The sponsors of the project subsequently supported its full implementation.

APL wrote in a press release on Sept. 17 that they feel that their expertise in detection algorithms makes them qualified to create applications that gather and analyze data.

The ESSENCE program is now available through an open source project called OpenESSENCE. The source code is available on GitHub, an independent open source code sharing website. OpenESSENCE is web-based so that it can be utilized and deployed in resource-scarce environments.

There is also a desktop variant of OpenESSENCE, the OpenESSENCE Desktop Edition (EDE), which allows usage of the system without internet access. Both systems allow the collection of data using a localized cell phone network.

In an infographic on its Suite for Automated Global Electronic bioSurveillance (SAGES) site, APL explained that the ESSENCE program collects health data such as hospital admission information. It utilizes detection algorithms that look at this data for indicators of disease outbreaks.

OpenESSENCE is part of the SAGES suite, which was released in July 2013. This suite includes OpenESSENCE, EDE and SAGES ODK-Collect, an Android application based on a third-party open source program called ODK-Collect. It was developed by APL to facilitate data entry into a SAGES program using custom forms with data sent over the SMS protocol, which is one of the ways that phones exchange short text messages. The SMS protocol is used because it is more widely available than common mobile data protocols and so that SAGES suites can be deployed in the most resource-scarce areas of the world.

Initial development of the EDE was named the Surveillance Tool for Analysis, Management, and Reporting data (STAMR) and started as an add-in application to EpiInfo. EpiInfo is public domain software developed by the CDC in 1985 for the collection and analysis of epidemiology data. STAMR was developed for the EpiInfo used by the Department of Health of the Republic of the Philippines and was made a globally utilized open source program after development had progressed sufficiently. EDE is built on the Eclipse Rich Client Platform (RCP), an open source program developed by third-party programmers to facilitate the development of applications which require heavy client-side data analysis. This platform allows replacement of the server-based analysis used by OpenESSENCE.

Because SAGES in an open source project, anyone can modify it and share their modifications freely. APL and the Global Emerging Infections Surveillance and Response System (GEIS) of the Armed Forces Health Surveillance Center (AFHSC) monitor the SAGES website in order to test these contributions so that particularly well-designed ones can be included in the official release.

Moving forward, APL will be developing systems for predictive analysis of potential future outbreaks. In the press release, Sheri Lewis, the Global Helath Surveillance program manager in APL’s Homeland Protection Mission Area, explained that APL technology could help researchers know what areas certain diseases are likely to occur in, as well as predict when they will flare up or become outbreaks. This could help public health officials with their strategies for containing the disease and curing people.