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Biosurveillance
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Methods and Case Studies
Methods and Case Studies

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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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+Biosurveillance is #15 of Amazon Best Sellers for the Epidemiology category

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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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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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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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You can preview the book's content online here...

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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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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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