Integrated Global Early Warning and Response System (Taha Kass-Hout and Nicolás di Tada)

Biosurveillance 2.0

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Most state and local health departments are involved in on-going traditional disease surveillance and are beginning to access information through health information exchange with clinical partners. Biosurveillance initiatives offer the opportunity to leverage these existing initiatives while providing important data to protect community health. Building on these existing activities and relationships is key to the success of national initiatives such as BioSense Redesign and meaningful use of electronic health records as a component of the evolving nationwide health information network (NHIN). During this session/workshop, the National Association of County and City Health Officials (NACCHO) and the Association of State and Territorial Health Officials (ASTHO) in association with the Centers for Disease Control and Prevention will address discuss the BioSense redesign effort and provide opportunities for extended engagement of local and state health officials. This workshop encourages the participation of public health emergency responders, and local public health personnel involved in bio-surveillance for emergency preparedness and response within their jurisdictions.

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Biosurveillance 2.0: Lecture at Emory University

As mandated in the Public Health Security and Bioterrorism Preparedness and Response Act of 2002, CDC’s BioSense program was launched in 2003 with the aim of establishing an integrated system of nationwide public health surveillance for the early detection and prompt assessment of potential bioterrorism-related illness. Over the following several years, as awareness grew about the limits of syndromic and related automated surveillance systems, including BioSense, in providing early and accurate epidemic alerts, increased emphasis was placed on their use in providing timely situation awareness throughout the course of public health emergencies. In practice, a key application of these systems has been their use in tracking the course of seasonal influenza and, in 2009, the impact of the H1N1 influenza pandemic. While retaining the original purpose of BioSense of early event (or threat) detection and characterization, we believe the most efficient and effective approach to achieve the program’s long-term business case is to build on existing systems and programs. This will have additional public health benefits that can improve the nation’s health at all times, including: 1. Public health situation awareness, 2. Routine public health practice, 3. Improving health outcomes and public health; and 4. Monitoring healthcare quality

BioSense Program Going Forward: HIMSS10 Conference

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Prospective anomaly detection methods such as the Modified EARS C2 are commonly adapted and used in public health syndromic surveillance systems. These methods however can produce an excessive false alert rate. We present a combined use of retrospective (e.g., Change Point Analysis (or CPA)) and prospective (e.g., C2) anomaly detection methods. This combined approach will help detect sudden aberrations in addition to subtle changes in local trends, help rule out alarm investigations, and assist with retrospective follow-ups. Examples on the utility of this combined approach in working collaboratively with the scientific community are applied to BioSense emergency departments' visits due to ILI. Methods, limitations, future work, and invitation to the scientific community to collaborate with us will be discussed at this talk.

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Big Data in Public Health

A hybrid (event-based and indicator-based) platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks or pandemics). The platform helps synthesize health-related event indicators from a wide variety of information sources (structured and unstructured) into a consolidated picture for analysis, maintenance of “community-wide coherence”, and collaboration processes. The platform offers features to detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease outbreak and provide decision makers with tools, methodologies and processes to investigate the event.

Riff: A Social Network and Collaborative Platform for Public Health Disease S...

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Biosurveillance 2.0

Evolve

Social Media for the Meta-Leader

Internet2 and Public Health Surveillance

Updates on the BioSense Program Redesign: 2011 Public Health Preparedness Summit

Novel Approaches in Public Health Surveillance

Collaboration Technology for Public Health and Humanitarian Action and Global...

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Epi Info™ Mesh4x

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Evolve: InSTEDD's Global Early Warning and Response System

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Change Point Analysis

Using Open Source Software For Public Health Kass-Hout Di Tada

Big Data in Public Health

Riff: A Social Network and Collaborative Platform for Public Health Disease S...

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Clinical documents are a repository of information about patients' conditions. However, this wealth of data is not properly tapped by the existing analysis tools. Dengue is one of the most widespread water borne diseases known today. Every year, dengue has been threatening lives the world over. Systems already developed have concentrated on extracting disorder mentions using dictionary look-up, or supervised learning methods. This project aims at performing Named Entity Recognition to extract disorder mentions, time expressions and other relevant features from clinical data. These can be used to build a model, which can in turn be used to predict the presence or absence of the disease, dengue. Further, we perform a frequency analysis which correlates the occurrence of dengue and the manifestation of its symptoms over the months. The system produces appreciable accuracy and serves as a valuable tool for medical experts.

DENGUE DETECTION AND PREDICTION SYSTEM USING DATA MINING WITH FREQUENCY ANALYSIS

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Cloud computing, a future generation concept characterized by three entities: Software, hardware & network designed to enhance the capacity building simultaneously increasing the throughput by extending the reach for any system without having heavy investment of infrastructure and training new personnel. It is becoming a major building block for any sort of businesses across the globe. This paper likes to propose a cloud as a solution for having an effective disease surveillance system. Till now, multiple surveillance systems come into play but still they lack sensitivity, specificity & timeliness.

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The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.

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Biosurveillance: Machine Learning And Disease Surveillance by Kass-Hout Di Tada

Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.

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This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms – i.e., for optimising and securing digital healthcare systems in anticipation of Disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.

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Disease outbreak detection, monitoring and notification systems are increasingly gaining popularity since these systems are designed to assess threats to public health and disease outbreaks are becoming increasingly common world-wide. A variety of systems are in use around the world, with coverage of national, international and global disease outbreaks. These systems use different taxonomies and classifications for the detection and prioritization of potential disease outbreaks. In this paper, we study and analyze the current disease outbreak systems. Subsequently, we extract features and functions of typical and generic disease outbreak systems. We then propose a generic model for disease outbreak notification systems. Our effort is directed towards standardizing the design process for typical disease outbreak systems.

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Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.

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Integrated Global Early Warning and Response System

Presenting precisionFDA for the first time at the Precision Medicine Coalition in Washington, DC on February 24, 2016 Any views or opinions expressed here do not necessarily represent the views of the FDA, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services.

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More from Taha Kass-Hout, MD, MS

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an update to ISDS 9th Annual Conference... As mandated in the Public Health Security and Bioterrorism Preparedness and Response Act of 2002, CDC's BioSense Program was launched in 2003 to establish an integrated national public health surveillance system for early detection and rapid assessment of potential bioterrorism-related illness: http://www.cdc.gov/biosense. Currently, the BioSense Program is undergoing redesign effort: http://biosenseredesign.org. The goal of the redesign is to be able to provide nationwide and regional situational awareness for all hazards health-related events (beyond bioterrorism) and to support national, state, and local responses to those events. Disclaimer: Any views or opinions expressed here do not necessarily represent the views of the CDC, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services.

Introducing BioSense Program Redesign

BioSense is an all-hazards surveillance program for achieving near real-time national public health situation awareness and early detection. Prospective anomaly detection methods such as the Modified EARS C2 are commonly adapted and used in BioSense and other public health syndromic surveillance systems. These methods however can produce an excessive false alert rate. Analyses results will be presented on the combined use of retrospective (e.g., Change Point Analysis (or CPA)) and prospective (e.g., C2) anomaly detection methods. This combined approach will help detect sudden aberrations in addition to subtle changes in local trends, help rule out alarm investigations, and assist with retrospective follow-ups. Examples on the utility of this combined approach in working collaboratively with the scientific community are applied to BioSense emergency departments' visits due to ILI. Methods, limitations, future work, and invitation to the scientific community to collaborate with us will be discussed at this talk.

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InSTEDD’s Mesh4x (http://code.google.com/p/mesh4x) allows for data synchronization among different data sources regardless of technology platform or network connectivity. Users can make their data available to all users in their distributed project team or across different jurisdictions. We describe the utility and architecture of Mesh4x to share data over the Internet cloud where users determine which subset of their data are exchanged. This technology raises the potential to share data (e.g., during outbreak investigation, disaster recovery or humanitarian relief efforts) where multiple people are then allowed access to see each other’s data, update the information as the event unfolds, and securely exchange data with one another.

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A near-real time data exchange between multiple instances of Epi Info™ was enabled by configuring Mesh4x (http://code.google.com/p/mesh4x/) for Internet cloud (e.g., Amazon’s EC2, Google cloud/App Engine) and for peer-to-peer (over SMS) synchronization. A client-based tool can easily be used by an epidemiologist to build and configure a mesh environment, without requiring prior technical knowledge.

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