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Biosurveillance: Methods and Case Studies
… solidly grounded in biosurveillance practice. … chapters describe some of the exciting new sources of data, including SMS text messaging, remote sensing, and even rumour-based information sources. … excellent background or motivational reading for advanced students entering the area. It provides up-to-date illustrations of where this fast-developing field is now.
David J. Hand, International Statistical Review, 2012

While having its roots in 21st-century infectious disease threats to health on a grand scale, biosurveillance has come to encompass a broader scope of the science and practice of managing population health-related data and information so that effective action can be taken to mitigate adverse health effects from urgent threats. This expansive scope is reflected in the diverse collection of reports and perspectives brought together in Biosurveillance: Methods and Case Studies. … This text provides an important venue for the sharing of ideas and engagement of health scientists and practitioners that will be needed to assure progress.
From the Foreword by Daniel M. Sosin, MD, MPH, Acting Director, Office of Public Health Preparedness and Response, Centers for Disease Control and Prevention
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The Role Of  Zoos In Biosurveillance 

"Zoos are excellent but overlooked urban #biosurveillance sites. Many of the routine activities of zoos lend themselves to sustainable surveillance for diseases of public health interest. This chapter describes several initiatives that have successfully bridged the gap between human and animal #disease #surveillance. These include the AZA Ungulate Tuberculosis Monitoring Program, The West Nile Virus Zoological Surveillance Project, and the USDA APHIS AZA Management Guidelines for #Avian #Influenza: Zoological Parks & Exhibitors Surveillance Plan. Given that there is no formal surveillance of cats, dogs, rodents and local #wildlife found in cities, it makes sense for public health to engage in partnership with #zoos for increased situation awareness of urban #zoonotic threats."

Julia Chosy, PhD
Research Epidemiologist
Davee Center of Epidemiology and Endocrinology
Lincoln Park Zoo

Janice Mladonicky
Epidemiology Intern
Davee Center of Epidemiology and Endocrinology
Lincoln Park Zoo

Tracey McNamara, DVM
Diplomat, ACVP
Professor, Pathology
College of Veterinary Medicine
Western University of Health Sciences

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books.google.com - As evidenced by the anthrax attacks in 2001, the SARS outbreak in 2003, and the H1N1 influenza pandemic in 2009, a pathogen does not recognize geographic or...
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HealthMap

Amy L. Sonricker (Hansen), MPH
Emergency Medicine, Children’s Hospital Boston Informatics Program, Boston, MA, USA

Clark C. Freifeld, MS
Children's Hospital Informatics Program at the Harvard–MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology Media Lab, Cambridge, MA, USA

Mikaela Keller, PhD
Emergency Medicine, Children’s Hospital Boston Informatics Program, Boston, MA, USA
Harvard Medical School, Boston, Massachusetts, USA

John S. Brownstein, PhD
Children's Hospital Informatics Program at the Harvard–MIT Division of Health Sciences and Technology
Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA


Unstructured electronic information sources, such as news reports, have proven to be valuable inputs for public health surveillance (Freifeld et al. 2008). In fact, the value of Web-based information for early disease detection, public health monitoring, and risk communication has never been as evident as it is today (Brownstein, Freifeld, and Madoff 2009).  The Internet has become a critical medium for clinicians, public health practitioners, and laypeople seeking health information. It has been estimated that 37-52% of Americans seek health-related information on the Internet each year, generally utilizing search engines to find advice on conditions, symptoms, and treatments (Brownstein, Freifeld, and Madoff 2009). Data about diseases and outbreaks are disseminated not only through online announcements by government agencies but also through informal channels, ranging from press reports to blogs to chat rooms to analyses of Web searches (Brownstein, Freifeld, and Madoff 2009). +HealthMap was developed with the aim of creating an integrated global view of emerging infectious diseases, based not solely on traditional public health datasets, but rather on a combination of available information sources to include these informal channels (Brownstein et al. 2008). The principal objective of HealthMap is to provide access to the greatest amount of potentially useful health information across the widest range of geography and pathogens, without inundating the user with excess information (Freifeld et al. 2008).
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As evidenced by the anthrax attacks in 2001, the SARS outbreak in 2003, and the H1N1 influenza pandemic in 2009, a pathogen does not recognize geographic or national boundaries, often leading to devastating consequences. Automated biosurveillance systems have emerged as key solutions for mitigating current and future health-related events. Focusing on this promising public health innovation, Biosurveillance: Methods and Case Studies discusses how these systems churn through vast amounts of health-related data to support epidemiologists and public health officials in the early identification, situation awareness, and response management of natural and man-made health-related events. By +Taha Kass-Hout
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Remote Sensing Based Modeling Of Infectious Disease Transmission

Richard K. Kiang1, Farida Adimi1,2, Radina P. Soebiyanto1,3
1 NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
2 Wyle International, McLean, Virginia, USA
3 University of Maryland at Baltimore County, Baltimore, Maryland, USA

Abstract Using remotely sensed data to model infectious disease transmission has become an increasingly popular and important technique. Quite a few infectious diseases have environmental and contextual determinants that can be measured with remote sensing. We use nine infectious diseases – malaria, Dengue Fever, West Nile Virus, Rift Valley Fever, filariasis, leishmaniasis, cholera, schistosomiasis, and avian influenza – to illustrate the geophysical parameters that influence transmission of disease. Some examples of these parameters include: precipitation, temperature, humidity, vegetation, ground cover and elevation. Both governmental space agencies and commercial organizations gather and offer remote sensing data. The remote sensing missions or sensors that provide these geophysical parameters are named here. Techniques to classify ground cover types (bodies of water, forests, rice fields, vegetation types, roads, dwellings, etc) from which the contextual determinants for disease transmission often can be extracted are also discussed. Modeling disease prevalence and transmission risks using remotely sensed geophysical parameters may be statistically or biologically based. The advantages for using each approach are touched upon here. A number of common and advanced modeling techniques, including those that utilize artificial intelligence, will also be discussed.
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Timeliness Of Data Sources

Lynne Dailey, PhD, MPH, BSc
Lecturer & Senior Policy and Planning Officer
Edith Cowan University
Perth, Western Australia

Abstract

Biosurveillance provides an alternative strategy for outbreak detection (Stoto et al. 2005). It is based on the rationale that there are identifiable behaviors exhibited early in the course of disease. The performance of an early warning system can be measured by three indices, namely sensitivity, specificity and timeliness (Buckeridge et al. 2005; Mandl et al. 2004). This chapter focuses on the aspect of timeliness, which is a metric that quantifies the time difference between two data sources used for surveillance.

Timeliness has been described using various methods with different levels of complexity. These include peak comparison, aberration detection comparison and correlation. Peak comparison involves comparing the temporal distance between the peaks in each data source. Aberration detection is based on the date of alert generated by an algorithm applied to each time series. In terms of correlation, timeliness is defined as the time lag at which the correlation between two data sources is significant (Johnson et al. 2004).

This chapter presents a review of alternative data sources for surveillance, reviews the time advantage or timeliness of each source, and discusses the strengths and weakness of the competing approaches for determining timeliness.
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+Biosurveillance is #15 of Amazon Best Sellers for the Epidemiology category
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Aberration Detection in R Illustrated by Danish Mortality Monitoring

Michael Höhle

"The R system is a free software environment for statistical computing and graphics distributed under a GNU-style copyleft license and running under Unix, Windows, and Mac (R Development Core Team, 2009). Several documents and books provide an introduction, such as Dalgaard (2008), Venables et al. (2009), and Muenchen (2009). The add-on package surveillance offers functionality for the visualization, monitoring, and simulation of count data time series in R for public health surveillance and biosurveillance. It provides an implementation of different aberration detection algorithms for epidemiologists and an infrastructure for developers of new algorithms. The package is freely available under the GNU GPL license and obtainable from the Comprehensive R Archive Network (CRAN)."

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Simulating and Evaluating Biosurveillance Datasets

+Thomas Lotze Applied Mathematics and Scientific Computation Program, University of Maryland, College Park, MD, USA

+Galit Shmueli Department of Decision, Operations & Information Technologies and Center for Health and Information Decision Systems, University of Maryland, College Park, MD, USA

+Inbal Yahav Robert H Smith School of Business, University of Maryland, College Park, MD, USA

Abstract
Biosurveillance involves monitoring measures of diagnostic and pre-diagnostic activity for early detection of disease outbreaks. Modern biosurveillance data include daily counts of diagnostic evidence such as lab results, and pre-diagnostic health seeking behavior such as medication sales. A serious challenge to research in the field of biosurveillance is the lack of available authentic data to researchers. This significantly limits the possibility of algorithm development and evaluation and hinders the comparison of methods across different groups of researchers. Since biosurveillance datasets are usually proprietary and tightly held by their owners, an alternative is generating simulated or semi-authentic data that are similar to authentic datasets. This paper describes a method for simulating multivariate biosurveillance time series, in the form of daily counts from multiple biosurveillance series, by using statistics from authentic biosurveillance data. Moreover, it uses statistical methods to test the validity of these simulated series, testing whether they could reasonably have come from the same distribution as the authentic series.
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Introduction

Since the dawn of our collective memory, plague and contagious disease has been a frightening, unseen, implacable enemy. It is not surprising that efforts to understand contagious illness extend back nearly as far as the written word. Hippocrates, the father of the western medical tradition, observed patterns of illness associated with temporal, geographic and environmental conditions. The Persian physician Avicenna, in the Eleventh Century, further speculated on the nature of contagion and disease spread, proposed the use of syndrome definitions for diagnosing diseases, and introduced the practice of quarantining. As the bubonic plague ravaged Europe and the Middle East in the 14th century, Islamic scholars advanced theories of tiny, unseen entities spreading the illness through contact, and by the 16th century, the germ theory of disease was taking root in Western Europe. John Graunt’s analysis of mortality rolls in the aftermath of the Great Plague of 1666 pioneered the use of statistical analysis for understanding the behavior of contagious disease. For most modern epidemiologists, the turning point was Dr. John Snow’s analysis of the Broad Street Cholera outbreak of 1854, which combined statistical and spatial analysis, allowing for the localization of the source of infection and management of the outbreak. The lesson became clear—the sooner an outbreak was identified and localized the greater the probability to contain and lessen the effects of the outbreak.

At the beginning of the 21st century, the shadow of a pandemic still casts fear across our modern generation. Several factors have conspired to increase the potential destructive power of epidemics. Both human and animal populations are densely packed in artificial environments. Pathogens have developed resistance to established treatments, and the Anthrax attacks of 2001 have illustrated that deliberate propagation of an infectious disease is a real threat in the modern age. The geographic barriers that have provided protection in the past have been bypassed. The geographical isolation between plants and animals has been gradually broken by the intentional or natural transport of organisms caused by human travel, tourism or trade. Foods and materials we use may come from anywhere around the globe. The acceleration of transportation has increased the speed and reach of a disease. Steam powered transport made it possible for the 1918 Influenza Pandemic to cross oceans within weeks. As evidenced by the SARS outbreak of 2003, an infection may now travel from continent to continent in a matter of hours. A Pathogen does not respect geographic or national boundaries. It is a global threat that all nations of the world need to face.

Although technology has contributed to the speed and distance which disease can spread, it also provides the key for meeting current and future epidemic threats. One of the most exciting innovations in public health is the development of automated biosurveillance systems that can churn through vast amounts of health-related data to support early identification, situation awareness, and response management for epidemiologists and public health officials. The aim of this book is to capture the story of these modern-day pioneers who are walking in John Snow’s footsteps, and to portray the current state of the art in biosurveillance, where some of the most promising aspects of modern information technology can be applied to this age-old challenge of combating the spread of disease and illness.

Despite the excitement that has greeted the initial generation of biosurveillance systems, several difficult challenges remain. As this book illustrates, biosurveillance operates in a complex, multidimensional problem space, and can be viewed from many different perspectives. Perhaps no single factor will affect the design of a biosurveillance system more than the data it uses. There is a tension between the most reliable sources of data (such as confirmed laboratory results) and the syndromic approaches which offer an earlier opportunity for detection (Emergency Department (ED) chief complaints, or over the counter sales). Animal data, vital statistics, and environmental data may identify the type of endemic relationships that Hippocrates first documented. Collecting and aggregating data from multiple sites raises issues of data granularity, quality, latency, and underlying meaning. Experience in developing detection algorithms has shown this to be a formidable undertaking, given the effort needed to obtain or create data sets for calibration, as well as tuning algorithms to reduce the false alarm rate.

Experience from fielding the first generation of biosurveillance systems, has shown a host of operational issues that need to be addressed for a successful system. One of the most frequently noted shortcomings was that the role of humans (epidemiologists, public health officials, and the system developers themselves), was not well defined as these systems primarily focused on automation. How were alerts to be validated and communicated? How could the system better support multiple organizations at the local, state/province, national, and even global level? What elements from the “Information Storm” are most important to convey accurate situation awareness at these different levels? How can such a system support different communities of interest; such as respiratory or foodborne illnesses, and different types of users? How easily can the system incorporate new data sources, and identify new types of threats? Practical considerations, particularly cost, are also important. Some existing systems are proving too costly to operate and maintain, let alone expand to incorporate more data sources. Quantifying the added value provided by a biosurveillance system will be necessary for increasing public investments to support them and making widespread deployment of these systems a reality.

Innovators today are pushing the envelope as to what we would consider a biosurveillance system. Some are taking advantage of new technologies such as web-crawling, remote sensing, and SMS texting to provide flexible, lower cost input channels for surveillance. Others are adopting a “system of systems” approach, where a given system will need to reason based on its own raw data as well as conclusions from other automated systems. The scope of these systems is also expanding from early detection to support the entire outbreak and management cycle. For Dr. Snow, this was easy, as he only needed to remove the handle of the water pump that was dispensing the contaminated water. For all of us in the global health community, this is a challenge we dare not ignore.

I have tried to organize this book practically, following natural sequence from theory to application. The initial chapters of the book build a foundation of knowledge for the reader. Later chapters contain more applied case studies written by experts from the field relating their practical experience within their professional domain. I would like to thank all of the authors in this volume for not only sharing their insights, but also their candid appraisals of lessons learned and unresolved issues that will help chart the future of biosurveillance. I would also like to thank the many dedicated experts and pioneers in the public health community who, although they may not be listed as authors in this collection, made indispensable contributions to our understanding of biosurveillance. I am also grateful to the many organizations and companies which have supported my involvement with biosurveillance over the years, including the US Centers for Disease Control and Prevention (CDC), Innovative Support to Emergencies, Diseases and Disasters (InSTEDD), the Rockefeller Foundation and Google.org.

+Taha Kass-Hout [Taha A. Kass-Hout, MD, MS] (with contributions from Walton J. Page, Jr.)

https://books.google.com/books?id=WVvo3npf_EoC&lpg=PP1&dq=biosurveillance&pg=PP1#v=onepage&q&f=false
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Methods and Case Studies
Introduction
Release Date: November 15, 2010

Genre: Public Health

Features
  • Provides a synopsis of current state-of-the-art practices as well as a starting point for the development and evaluation of new methods
  • Covers applied research and complete case studies that focus on local, regional, national, and international implementation
  • Presents techniques from other fields, such as intelligence and engineering
  • Explores future innovations in biosurveillance, including advances in analytical methods, modeling, and simulation
  • Addresses policy and organizational issues related to the construction of biosurveillance systems
Summary

As evidenced by the anthrax attacks in 2001, the SARS outbreak in 2003, and the H1N1 influenza pandemic in 2009, a pathogen does not recognize geographic or national boundaries, often leading to devastating consequences. Automated biosurveillance systems have emerged as key solutions for mitigating current and future health-related events. Focusing on this promising public health innovation, Biosurveillance: Methods and Case Studies discusses how these systems churn through vast amounts of health-related data to support epidemiologists and public health officials in the early identification, situation awareness, and response management of natural and man-made health-related events.

The book follows a natural sequence from theory to application. The initial chapters build a foundation while subsequent chapters present more applied case studies from around the world, including China, the United States, Denmark, and the Asia-Pacific region. The contributors share candid, first-hand insights on lessons learned and unresolved issues that will help chart the future of biosurveillance.

As this book illustrates, biosurveillance operates in a complex, multidimensional problem space that incorporates varied data. Capturing the progress of modern-day pioneers who are walking in John Snow’s footsteps, this volume shows how contemporary information technology can be applied to the age-old challenge of combating the spread of disease and illness.

Table of Contents
  1. Timeliness of Data Sources, Lynne Dailey
  2. Simulating and Evaluating Biosurveillance Datasets, Thomas H. Lotze, Galit Shmueli, Yahav Inbal, and Robert H. Smith
  3. Remote Sensing-Based Modeling of Infectious Disease Transmission, Richard K. Kiang, Farida Adimi, and Radina P. Soebiyanto
  4. Integrating Human Capabilities into Biosurveillance Systems: A Study of Biosurveillance and Situation Awareness, Cheryl A. Bolstad, Haydee M. Cuevas, Jingjing Wang-Costello, Mica R. Endsley, Walton John Page, and Taha Kass-Hout
  5. The Role of Zoos in Biosurveillance, Julia Chosy, Janice Mladonicky, and Tracey McNamara
  6. HealthMap, Amy L. Sonricker, MPH, Clark C. Freifeld, Mikaela Keller, and John S. Brownstein
  7. The Role of SMS Text Messaging to Improve Public Health Response, Elizabeth Avery Gomez
  8. Using Prediction Markets to Forecast Infectious Diseases, Philip M. Polgreen and Forrest D. Nelson
  9. The Role of Data Aggregation in Public Health and Food Safety Surveillance, Artur Dubrawski
  10. Introduction to China’s Infectious Disease Surveillance System, Jin Shuigao and Ma Jiaqi
  11. Biosurveillance and Public Health Practice: A Case Study of North Carolina’s NC DETECT System, S. Cornelia Kaydos-Daniels, Lucia Rojas Smith, Amy I. Ising, Clifton Barnett, Tonya Farris, Anna E. Waller, and Scott Wetterhall
  12. Aberration Detection in R Illustrated by Danish Mortality Monitoring, Michael Höhle and, Anne Mazick
  13. User Requirements toward a Real-Time Biosurveillance Program, Nuwan Waidyanatha and Suma Prashant
  14. Using Common Alerting Protocol to Support a Real-Time Biosurveillance Program in Sri Lanka and India, Gordon A. Gow and Nuwan Waidyanatha
  15. Navigating the Information Storm: Web-Based Global Health Surveillance in BioCaster, Nigel Collier, Son Doan, Reiko Matsuda Goodwin, John McCrae, Mike Conway, Mika Shigematsu, and Ai Kawazoe
  16. A Snapshot of Situation Awareness: Using the NC DETECT System to Monitor the 2007 Heat Wave, David B. Rein
  17. Linking Detection to Effective Response, Scott F. Wetterhall, Taha Kass-Hout, and David L. Buckeridge

Author Biography

Taha A. Kass-Hout, MD, MS (At the time this book was published in 2010) Taha serves as the Deputy Director for Information Science and the BioSense Program Manager for the Division of Notifiable Diseases and Healthcare Information at CDC’s Public Health Surveillance Program Office. He has over 15 years of experience in health, public health, and informatics. Additionally, at CDC, he manages the surveillance project tracking the 2nd wave of H1N1 pandemic. Developed in collaboration between the International Society for Disease Surveillance (ISDS) and CDC after the first wave of H1N1, the system tracked over 67 million visits encompassing >40% of total emergency departments in the US with >140,000 visits/day from April 2009 through February 2010. This system was acknowledged in December 2009 by the White House Office of Science, and Technology Policy as a model case study for Open Government based on its voluntary participation, low cost to acquire data, and unprecedented public transparency. During the response to the 2003 SARS outbreak, Taha led the U.S. informatics and information task at the U.S. National Center for Infectious Diseases at CDC.

He is credited with the following Innovations:

  • InSTEDD’s Riff, [InSTEDD was founded by Google in 2006], an Open Source Social Networking Platform for Integrated Early Warning and Response. On Jan 17 2010, the Thomson Reuters Foundation used Riff to launch a first-of-its kind, free disaster-information service for the people of Port Au Prince, Haiti. This allowed survivors of Haiti's earthquake to receive critical information by text message directly to their phones, free of charge.
  • The Global Disease Surveillance Platform (GDSP™), patent pending (WO/2008/013553) a situation awareness platform to help predict, monitor, detect early, and enable timely response to national and global public health threats; such as a pandemic influenza.
  • eQuest: a just-in-time web-based survey creation and analysis solution used for epidemiologic and disease outbreak investigation. eQuest was the primary tool used by field epidemiologists during the investigation of the 2003 global SARS outbreak. eQuest has supported hundreds of disease outbreak investigations and public health field studies conducted by various public health agencies at all levels.

Taha holds Doctor of Medicine degree from the University of Texas, Health Sciences Center, Houston, Texas, and a Master of Science from the University of Texas, School of Public Health, Department of Biostatistics. In addition, he has had clinical training at Harvard's Beth Israel Deaconess Medical Center and the University of Texas Health Sciences Center at Houston.

Xiaohui Zhang is the president of International Public Health Institute, a nonprofit organization. For over 20 years, Dr. Zhang has led the scientific effort in the development of infectious disease surveillance systems, disease outbreak early detection and early warning systems, public health emergency preparedness and response systems, and information systems for comprehensive health care management. He has authored more than 40 publications in information technology, disease surveillance, decision making support, operational research, environmental modeling, artificial intelligence, simulation, and electrical engineering.

ISBN: 9781439800461
Publisher: Taylor and Francis, Chapman & Hall (CRC)

Editorial Reviews

*Biosurveillance: Methods and Case Studies*
_… solidly grounded in biosurveillance practice. … chapters describe some of the exciting new sources of data, including SMS text messaging, remote sensing, and even rumour-based information sources. … excellent background or motivational reading for advanced students entering the area. It provides up-to-date illustrations of where this fast-developing field is now._
*David J. Hand, International Statistical Review, 2012*

“While having its roots in 21st-century infectious disease threats to health on a grand scale, biosurveillance has come to encompass a broader scope of the science and practice of managing population health-related data and information so that effective action can be taken to mitigate adverse health effects from urgent threats. This expansive scope is reflected in the diverse collection of reports and perspectives brought together in Biosurveillance: Methods and Case Studies. … This text provides an important venue for the sharing of ideas and engagement of health scientists and practitioners that will be needed to assure progress.”

From the Foreword by Daniel M. Sosin, MD, MPH, Acting Director, Office of Public Health Preparedness and Response, Centers for Disease Control and Prevention

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Note: Thoughts expressed here are my own (not necessarily my employer's views). Opinions in comments posted by other people are just that, opinions and comments from other people and therefore may not reflect my views. Posts of links to external content do not imply endorsement, unless explicitly stated. Posts of product names, trade names, images, or commercial sources are for identification purposes only, and do not imply endorsement.
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