An Implementation Strategy to Develop Sustainable Surveillance Activities Through Adoption of a Target Operating Model.

The increasing threat of emerging and re-emerging pathogens calls for a shared vision toward developing and maintaining global surveillance mechanisms to enable rapid characterization of pathogens, a foundational requirement for effective outbreak response. Efforts establishing new surveillance programs in low- and middle-income countries (LMICs) have repeatedly led to siloed systems that prove unsustainable or ineffective due to narrowly focused approaches, competing priorities, or lack of resourcing. Barriers inherent to LMICs, such as resource limitations, workforce strain, unreliable supply chains, and lack of enduring champions exacerbate implementation and sustainability challenges. In order to improve adoption and endurance of new surveillance programs, more effective design and implementation of programs is needed to adequately reflect stakeholder needs and simultaneously support population-level disease monitoring and clinical decision-making across a range of chronic and acute health issues. At the heart of this cross-sectorial integration between clinical care and public health initiatives are emerging technologies and data modalities, including sequencing data. In this prospective, we propose an implementation strategy for genomics-based surveillance initiatives in LMICs founded on the use of a target operating model. Adoption of a target operating model for the design and implementation of genomic surveillance programs will ensure programs are agile, relevant, and unified across diverse stakeholder communities, thereby increasing their overall impact and sustainability.

Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE): Overview, Components, and Public Health Applications.

BACKGROUND: The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor health indicators of public health importance for the detection and tracking of disease outbreaks, consequences of severe weather, and other events of concern. The ESSENCE concept began in an internally funded project at the Johns Hopkins University Applied Physics Laboratory, advanced with funding from the State of Maryland, and broadened in 1999 as a collaboration with the Walter Reed Army Institute for Research. Versions of the system have been further developed by Johns Hopkins University Applied Physics Laboratory in multiple military and civilian programs for the timely detection and tracking of health threats. OBJECTIVE: This study aims to describe the components and development of a biosurveillance system increasingly coordinating all-hazards health surveillance and infectious disease monitoring among large and small health departments, to list the key features and lessons learned in the growth of this system, and to describe the range of initiatives and accomplishments of local epidemiologists using it. METHODS: The features of ESSENCE include spatial and temporal statistical alerting, custom querying, user-defined alert notifications, geographical mapping, remote data capture, and event communications. To expedite visualization, configurable and interactive modes of data stratification and filtering, graphical and tabular customization, user preference management, and sharing features allow users to query data and view geographic representations, time series and data details pages, and reports. These features allow ESSENCE users to gather and organize the resulting wealth of information into a coherent view of population health status and communicate findings among users. RESULTS: The resulting broad utility, applicability, and adaptability of this system led to the adoption of ESSENCE by the Centers for Disease Control and Prevention, numerous state and local health departments, and the Department of Defense, both nationally and globally. The open-source version of Suite for Automated Global Electronic bioSurveillance is available for global, resource-limited settings. Resourceful users of the US National Syndromic Surveillance Program ESSENCE have applied it to the surveillance of infectious diseases, severe weather and natural disaster events, mass gatherings, chronic diseases and mental health, and injury and substance abuse. CONCLUSIONS: With emerging high-consequence communicable diseases and other health conditions, the continued user requirement-driven enhancements of ESSENCE demonstrate an adaptable disease surveillance capability focused on the everyday needs of public health. The challenge of a live system for widely distributed users with multiple different data sources and high throughput requirements has driven a novel, evolving architecture design.

Mass gathering enhanced syndromic surveillance for the 8th Micronesian Games in 2014, Pohnpei State, Federated States of Micronesia.

Pohnpei State's Division of Primary Health Care implemented enhanced surveillance for early warning and detection of disease to support the 8th Micronesian Games (the Games) in July 2014. The surveillance comprised 11 point-of-care sentinel sites around Pohnpei, Federated States of Micronesia, collecting data daily for eight syndromes using standard case definitions. Each sentinel site reported total acute care encounters, total syndrome cases and the total for each syndrome. A public health response, including epidemiological investigation and laboratory testing, followed when syndrome counts reached predetermined threshold levels. The surveillance was implemented using the web-based Suite for Automated Global Electronic bioSurveillance Open-ESSENCE (SAGES-OE) application that was customized for the Games. Data were summarized in daily situation reports (SitReps) issued to key stakeholders and posted on PacNet, a Pacific public health e-mail network. Influenza-like illness (ILI) was the most common syndrome reported (55%, n = 225). Most syndrome cases (75%) were among people from Pohnpei. Only 30 cases out of a total of 408 syndrome cases (7%) presented with acute fever and rash, despite the large and ongoing measles outbreak at the time. No new infectious disease outbreak was recorded during the Games. Peaks in diarrhoeal and ILI cases were followed up and did not result in widespread transmission. The technology was a key feature of the enhanced surveillance. The introduction of the web-based tool greatly improved the timeliness of data entry, analysis and SitRep dissemination, providing assurance to the Games organizers that communicable diseases would not adversely impact the Games.

Enhanced surveillance for the Third United Nations Conference on Small Island Developing States, Apia, Samoa, September 2014.

The Ministry of Health in Samoa, in partnership with the Pacific Community, successfully implemented enhanced surveillance for the high-profile Third United Nations Conference on Small Island Developing States held concurrently with the popular local Teuila festival during a widespread chikungunya outbreak in September 2014. Samoa's weekly syndromic surveillance system was expanded to 12 syndromes and 10 sentinel sites from four syndromes and seven sentinel sites; sites included the national hospital, four private health clinics and three national health service clinics. Daily situation reports were produced and were disseminated through PacNet (the e-mail alert and communication tool of the Pacific Public Health Surveillance Network) together with daily prioritized line lists of syndrome activity to facilitate rapid response and investigation by the Samoan EpiNet team. Standard operating procedures for surveillance and response were introduced, together with a sustainability plan, including a monitoring and evaluation framework, to facilitate the transition of the mass gathering surveillance improvements to routine surveillance. The enhanced surveillance performed well, providing vital disease early warning and health security assurance. A total of 2386 encounters and 708 syndrome cases were reported. Influenza-like illness was the most frequently seen syndrome (17%). No new infectious disease outbreaks were recorded. The experience emphasized: (1) the need for a long lead time to pilot the surveillance enhancements and to maximize their sustainability; (2) the importance of good communication between key stakeholders; and (3) having sufficient staff dedicated to both surveillance and response.

Understanding How the "Open" of Open Source Software (OSS) Will Improve Global Health Security.

Improving global health security will require bold action in all corners of the world, particularly in developing settings, where poverty often contributes to an increase in emerging infectious diseases. In order to mitigate the impact of emerging pandemic threats, enhanced disease surveillance is needed to improve early detection and rapid response to outbreaks. However, the technology to facilitate this surveillance is often unattainable because of high costs, software and hardware maintenance needs, limited technical competence among public health officials, and internet connectivity challenges experienced in the field. One potential solution is to leverage open source software, a concept that is unfortunately often misunderstood. This article describes the principles and characteristics of open source software and how it may be applied to solve global health security challenges.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

BACKGROUND: Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. METHODS: We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. RESULTS: Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. CONCLUSIONS: A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Prediction of high incidence of dengue in the Philippines.

BACKGROUND: Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. METHODS: Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. PRINCIPAL FINDINGS: Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. CONCLUSIONS: This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.

Contributions of the Global Emerging Infections Surveillance and Response System Network to global health security in 2011.

In its 15th year, the Global Emerging Infections Surveillance and Response System (GEIS) continued to make significant contributions to global public health and emerging infectious disease surveillance worldwide. As a division of the US Department of Defense's Armed Forces Health Surveillance Center since 2008, GEIS coordinated a network of surveillance and response activities through collaborations with 33 partners in 76 countries. The GEIS was involved in 73 outbreak responses in fiscal year 2011. Significant laboratory capacity-building initiatives were undertaken with 53 foreign health, agriculture and/or defense ministries, as well as with other US government entities and international institutions, including support for numerous national influenza centers. Equally important, a variety of epidemiologic training endeavors reached over 4,500 individuals in 96 countries. Collectively, these activities enhanced the ability of partner countries and the US military to make decisions about biological threats and design programs to protect global public health as well as global health security.

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 collaborative experience of creating the National Capital Region Disease Surveillance Network.

The Johns Hopkins University Applied Physics Laboratory (JHU/APL) implemented state and district surveillance nodes in a central aggregated node in the National Capital Region (NCR). Within this network, de-identified health information is integrated with other indicator data and is made available to local and state health departments for enhanced disease surveillance. Aggregated data made available to the central node enable public health practitioners to observe abnormal behavior of health indicators spanning jurisdictions and view geographical spread of outbreaks across regions.Forming a steering committee, the NCR Enhanced Surveillance Operating Group (ESOG), was key to overcoming several data-sharing issues. The committee was composed of epidemiologists and key public health practitioners from the 3 jurisdictions. The ESOG facilitated early system development and signing of the cross-jurisdictional data-sharing agreement. This agreement was the first of its kind at the time and provided the legal foundation for sharing aggregated health information across state/district boundaries for electronic disease surveillance.Electronic surveillance system for the early notification of community-based epidemics provides NCR users with a comprehensive regional view to ascertain the spread of disease, estimate resource needs, and implement control measures. This article aims to describe the creation of the NCR Disease Surveillance Network as an exceptional example of cooperation and potential that exists for regional surveillance activities.

The AFHSC-Division of GEIS Operations Predictive Surveillance Program: a multidisciplinary approach for the early detection and response to disease outbreaks.

The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program's ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia.

Capacity-building efforts by the AFHSC-GEIS program.

Capacity-building initiatives related to public health are defined as developing laboratory infrastructure, strengthening host-country disease surveillance initiatives, transferring technical expertise and training personnel. These initiatives represented a major piece of the Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) contributions to worldwide emerging infectious disease (EID) surveillance and response. Capacity-building initiatives were undertaken with over 80 local and regional Ministries of Health, Agriculture and Defense, as well as other government entities and institutions worldwide. The efforts supported at least 52 national influenza centers and other country-specific influenza, regional and U.S.-based EID reference laboratories (44 civilian, eight military) in 46 countries worldwide. Equally important, reference testing, laboratory infrastructure and equipment support was provided to over 500 field sites in 74 countries worldwide from October 2008 to September 2009. These activities allowed countries to better meet the milestones of implementation of the 2005 International Health Regulations and complemented many initiatives undertaken by other U.S. government agencies, such as the U.S. Department of Health and Human Services, the U.S. Agency for International Development and the U.S. Department of State.

Utility of the ESSENCE Surveillance System in Monitoring the H1N1 Outbreak.

The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) enables health care practitioners to detect and monitor health indicators of public health importance. ESSENCE is used by public health departments in the National Capital Region (NCR); a cross-jurisdictional data sharing agreement has allowed cooperative health information sharing in the region since 2004. Emergency department visits for influenza-like illness (ILI) in the NCR from 2008 are compared to those of 2009. Important differences in the rates, timing, and demographic composition of ILI visits were found. By monitoring a regional surveillance system, public health practitioners had an increased ability to understand the magnitude and character of different ILI outbreaks. This increased ability provided crucial community-level information on which to base response and control measures for the novel 2009 H1N1 influenza outbreak. This report underscores the utility of automated surveillance systems in monitoring community-based outbreaks. There are several limitations in this study that are inherent with syndrome-based surveillance, including utilizing chief complaints versus confirmed laboratory data, discerning real disease versus those healthcare-seeking behaviors driven by panic, and reliance on visit counts versus visit rates.

INFOSHARE - An Information Sharing Tool for Public Health during the 2009 Presidential Inauguration and H1N1 Outbreak.

The 2009 Presidential Inauguration and H1N1 outbreak called for real-time electronic information-sharing and surveillance across multiple jurisdictions to better understand the health of migrating populations. The InfoShare web application proved to be an efficient tool for users to share disease surveillance information. During both high profile events, public health users shared information within a secure access-controlled website across regions in the U.S. and among agencies. Due to its flexible design, InfoShare was quickly modified from its 2009 Inauguration interface to an interface that supports H1N1 surveillance. Through discussions and post-use surveys, a majority of InfoShare users revealed that the tool had provided a valuable and needed function. InfoShare allowed individual jurisdictions to receive timely and useful information, which, when merged with neighboring jurisdictions, significantly enhanced situational awareness for better decision-making and improved public health outcomes.

Applying information and communications technologies to collect health data from remote settings: a systematic assessment of current technologies.

Modern information and communications technologies (ICTs) are now so feature-rich and widely available that they can be used to "capture," or collect and transmit, health data from remote settings. Electronic data capture can reduce the time necessary to notify public health authorities, and provide important baseline information. A number of electronic health data capture systems based on specific ICTs have been developed for remote areas. We expand on that body of work by defining and applying an assessment process to characterize ICTs for remote-area health data capture. The process is based on technical criteria, and assesses the feasibility and effectiveness of specific technologies according to the resources and constraints of a given setting. Our characterization of current ICTs compares different system architectures for remote-area health data capture systems. Ultimately, we believe that our criteria-based assessment process will remain useful for characterizing future ICTs.

Electronic public health surveillance in developing settings: meeting summary.

In some high-income countries, public health surveillance includes systems that use computer and information technology to monitor health data in near-real time, facilitating timely outbreak detection and situational awareness. In September 2007, a meeting convened in Bangkok, Thailand to consider the adaptation of near-real time surveillance methods to developing settings. Thirty-five participants represented Ministries of Health, universities, and militaries in 13 countries, and the World Health Organization (WHO). The keynote presentation by a WHO official underscored the importance of improved national capacity for epidemic surveillance and response under the new International Health Regulations, which entered into force in June 2007. Other speakers presented innovative electronic surveillance systems for outbreak detection and disease reporting in developing countries, and methodologies employed in near-real time surveillance systems in the United States. During facilitated small- and large-group discussion, participants identified key considerations in four areas for adapting near-real time surveillance to developing settings: software, professional networking, training, and data acquisition and processing. This meeting was a first step in extending the benefits of near-real time surveillance to developing settings. Subsequent steps should include identifying funding and partnerships to pilot-test near-real time surveillance methods in developing areas.