Developing open source, self-contained disease surveillance software applications for use in resource-limited settings.

BACKGROUND: Emerging public health threats often originate in resource-limited countries. In recognition of this fact, the World Health Organization issued revised International Health Regulations in 2005, which call for significantly increased reporting and response capabilities for all signatory nations. Electronic biosurveillance systems can improve the timeliness of public health data collection, aid in the early detection of and response to disease outbreaks, and enhance situational awareness. METHODS: As components of its Suite for Automated Global bioSurveillance (SAGES) program, The Johns Hopkins University Applied Physics Laboratory developed two open-source, electronic biosurveillance systems for use in resource-limited settings. OpenESSENCE provides web-based data entry, analysis, and reporting. ESSENCE Desktop Edition provides similar capabilities for settings without internet access. Both systems may be configured to collect data using locally available cell phone technologies. RESULTS: ESSENCE Desktop Edition has been deployed for two years in the Republic of the Philippines. Local health clinics have rapidly adopted the new technology to provide daily reporting, thus eliminating the two-to-three week data lag of the previous paper-based system. CONCLUSIONS: OpenESSENCE and ESSENCE Desktop Edition are two open-source software products with the capability of significantly improving disease surveillance in a wide range of resource-limited settings. These products, and other emerging surveillance technologies, can assist resource-limited countries compliance with the revised International Health Regulations.

SAGES: a suite of freely-available software tools for electronic disease surveillance in resource-limited settings.

Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.

The pandemic influenza policy model: a planning tool for military public health officials.

The Pandemic Influenza Policy Model (PIPM) is a collaborative computer modeling effort between the U.S. Department of Defense (DoD) and the Johns Hopkins University Applied Physics Laboratory. Many helpful computer simulations exist for examining the propagation of pandemic influenza in civilian populations. We believe the mission-oriented nature and structured social composition of military installations may result in pandemic influenza intervention strategies that differ from those recommended for civilian populations. Intervention strategies may differ between military bases because of differences in mission, location, or composition of the population at risk. The PIPM is a web-accessible, user-configurable, installation-specific disease model allowing military planners to evaluate various intervention strategies. Innovations in the PIPM include expanding on the mathematics of prior stochastic models, using military-specific social network epidemiology, utilization of DoD personnel databases to more accurately characterize the population at risk, and the incorporation of possible interventions, e.g., pneumococcal vaccine, not examined in previous models.