Data-driven Disease Surveillance

The recent and still ongoing pandemic of SARS-CoV-2 has shown that an infectious disease outbreak can have serious consequences on public health and economy. In this situation, public health officials constantly aim to control and reduce the number of infections in order to avoid overburdening health care system. Besides minimizing personal contact through political measures, a fundamental approach to contain the spread of diseases is to isolate infected individuals. The effectiveness of the latter approach strongly depends on a timely detection of the outbreak as the tracking of individuals can quickly become infeasible when the number of cases increases. Hence, a key factor in the containment of an infectious disease is the early detection of a potential larger outbreak, commonly known as outbreak detection. For this purpose, epidemiologists rely on a variety of statistical surveillance methods in order to maintain an overview of the current situation of infections by either monitoring confirmed cases or cases with early symptoms. Mainly based on statistical hypothesis testing, these methods automatically raise an alarm if an unexpected increase in the number of infections is observed. The practical usefulness of such methods highly depends on the trade-off between the ability to detect outbreaks and the chances of raising a false alarm. However, this hypothesis-based approach to disease surveillance has several limitations. On the one hand, it is a hand-crafted approach which requires domain knowledge to set up the statistical methods, especially if early symptoms are monitored. On the other hand, outbreaks of emerging infectious diseases with different symptom patterns are likely to be missed by such a surveillance system. In this thesis, we focus on data-driven disease surveillance and address these challenges in the following ways. To support epidemiologists in the process of defining reliable disease patterns for monitoring cases with early symptoms, we present a novel approach to discover such patterns in historic data. With respect to supervised learning, we propose a fusion classifier which can combine the output of multiple statistical methods using the univariate time series of infection counts as the only source of information. In addition, we develop algorithms based on unsupervised learning which frame the task of outbreak detection as a general anomaly detection task. This even includes the surveillance of emerging infectious diseases. Therefore, we contribute a novel framework and propose a new approach based on sum-product networks to monitor multiple disease patterns simultaneously. Our results show that data-driven approaches are ideal to assist epidemiologists by processing large amounts of data that cannot fully be understood and analyzed by humans. Most significantly, the incorporation of additional information into the surveillance through machine learning techniques shows reliable and promising results

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom

The term "Geographic Information Systems" (GIS) has been added to MeSH in 2003, a step reflecting the importance and growing use of GIS in health and healthcare research and practices. GIS have much more to offer than the obvious digital cartography (map) functions. From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse changes, as well as sentinel events. Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS). This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide. The ultimate goal is to illuminate the road towards implementing a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time. The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels

Compilation of thesis abstracts, June 2007

NPS Class of June 2007This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750

Connecting the Dots. Intelligence and Law Enforcement since 9/11

This work examines how the conceptualization of knowledge as both problem and solution reconfigured intelligence and law enforcement after 9/11. The idea was that more information should be collected, and better analyzed. If the intelligence that resulted was shared, then terrorists could be identified, their acts predicted, and ultimately prevented. Law enforcement entered into this scenario in the United States, and internationally. “Policing terrorism” refers to the engagement of state and local law enforcement in intelligence, as well as approaching terrorism as a legal crime, in addition to or as opposed to an act of war. Two venues are explored: fusion centers in the United States and the international organization of police, Interpol. The configuration can be thought of schematically as operating through the set of law, discipline and security. Intelligence is predominantly a security approach. It modulates that within its purview, wielding the techniques and technologies that are here discussed. The dissertation is divided into two sections: Intelligence and Policing Terrorism. In the first, intelligence is taken up as a term, and its changes in referent and concept are examined. The Preface and Chapter One present a general introduction to the contemporary situation and intelligence, via Sherman Kent, as knowledge, organization and activities. Chapter Two traces the development of intelligence in the United States as a craft and profession. Chapter Three discusses some of the issues involving the intersection of intelligence and policy, and how those manifested in the aftermath of 9/11 and the lead up to the 2003 invasion of Iraq. The second section examines the turn to policing terrorism, beginning, in Chapter Four, with how Interpol has dealt with bioterrorism, and an examination of the shifting conceptualization of biological threats in international law. Moving from threats to their consequences, Chapter Five takes up the concept of an event in order to analyze the common comparison of Pearl Harbor and 9/11. Chapters Six and Seven turn to fieldwork done in the United States, with an examination of the suspicious activity reporting system and law enforcement’s inclusion in the Information Sharing Environment, focusing on fusion centers and data mining