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This book on Infectious Disease Informatics (IDI) and
biosurveillance is intended to provide an integrated view of the
current state of the art, identify technical and policy challenges
and opportunities, and promote cross-disciplinary research that
takes advantage of novel methodology and what we have learned from
innovative applications. This book also fills a systemic gap in the
literature by emphasizing informatics driven perspectives (e.g.,
information system design, data standards, computational aspects of
biosurveillance algorithms, and system evaluation). Finally, this
book attempts to reach policy makers and practitioners through the
clear and effective communication of recent research findings in
the context of case studies in IDI and biosurveillance, providing
"hands-on" in-depth opportunities to practitioners to increase
their understanding of value, applicability, and limitations of
technical solutions. This book collects the state of the art
research and modern perspectives of distinguished individuals and
research groups on cutting-edge IDI technical and policy research
and its application in biosurveillance. The contributed chapters
are grouped into three units. Unit I provides an overview of recent
biosurveillance research while highlighting the relevant legal and
policy structures in the context of IDI and biosurveillance ongoing
activities. It also identifies IDI data sources while addressing
information collection, sharing, and dissemination issues as well
as ethical considerations. Unit II contains survey chapters on the
types of surveillance methods used to analyze IDI data in the
context of public health and bioterrorism. Specific computational
techniques covered include: text mining, time series analysis,
multiple data streams methods, ensembles of surveillance methods,
spatial analysis and visualization, social network analysis, and
agent-based simulation. Unit III examines IT and decision support
for public health event response and bio-defense. Practical lessons
learned in developing public health and biosurveillance systems,
technology adoption, and syndromic surveillance for large events
are discussed. The goal of this book is to provide an
understandable interdisciplinary IDI and biosurveillance reference
either used as a standalone textbook or reference for students,
researchers, and practitioners in public health, veterinary
medicine, biostatistics, information systems, computer science, and
public administration and policy.
The goal of this book is to search for a balance between simple and
analyzable models and unsolvable models which are capable of
addressing important questions on population biology. Part I
focusses on single species simple models including those which have
been used to predict the growth of human and animal population in
the past. Single population models are, in some sense, the building
blocks of more realistic models -- the subject of Part II. Their
role is fundamental to the study of ecological and demographic
processes including the role of population structure and spatial
heterogeneity -- the subject of Part III. This book, which will
include both examples and exercises, is of use to practitioners,
graduate students, and scientists working in the field.
The book is a comprehensive, self-contained introduction to the
mathematical modeling and analysis of disease transmission models.
It includes (i) an introduction to the main concepts of
compartmental models including models with heterogeneous mixing of
individuals and models for vector-transmitted diseases, (ii) a
detailed analysis of models for important specific diseases,
including tuberculosis, HIV/AIDS, influenza, Ebola virus disease,
malaria, dengue fever and the Zika virus, (iii) an introduction to
more advanced mathematical topics, including age structure, spatial
structure, and mobility, and (iv) some challenges and opportunities
for the future. There are exercises of varying degrees of
difficulty, and projects leading to new research directions. For
the benefit of public health professionals whose contact with
mathematics may not be recent, there is an appendix covering the
necessary mathematical background. There are indications which
sections require a strong mathematical background so that the book
can be useful for both mathematical modelers and public health
professionals.
This book grew out of the discussions and presentations that began during the Workshop on Emerging and Reemerging Diseases (May 17-21, 1999) sponsored by the Institute for Mathematics and its Application (IMA) at the University of Minnesota with the support of NIH and NSF. The workshop started with a two-day tutorial session directed to ecologists, epidemiologists, immunologists, mathematicians, and scientists interested in the study of disease dynamics. The core of this second volume, Volume 126, covers research contributions on the use of dynamical systems (deterministic discrete, delay, PDEs, and ODEs models) and stochastic models in disease dynamics. Contributions motivated by the study of diseases like influenza, HIV, tuberculosis, and macroparasitic like schistosomiasis are also included. This second volume requires additional mathematical sophistication, and graduate students in applied mathematics, scientists in the natural, social, and health sciences, or mathematicians who want to enter the field of mathematical and theoretical epidemiology will find it useful. The collection of contributors includes many who have been in the forefront of the development of the subject.
This book grew out of the discussions and presentations that began during the Workshop on Emerging and Reemerging Diseases (May 17-21, 1999) sponsored by the Institute for Mathematics and its Application (IMA) at the University of Minnesota with the support of NIH and NSF. The workshop started with a two-day tutorial session directed to ecologists, epidemiologists, immunologists, mathematicians, and scientists interested in the study of disease dynamics. The core of this first volume, Volume 125, covers tutorial and research contributions on the use of dynamical systems (deterministic discrete, delay, PDEs, and ODEs models) and stochastic models in disease dynamics. The volume includes the study of cancer, HIV, pertussis, and tuberculosis. Beginning graduate students in applied mathematics, scientists in the natural, social, or health sciences or mathematicians who want to enter the fields of mathematical and theoretical epidemiology will find this book useful.
Mathematical and Statistical Estimation Approaches in Epidemiology
compiles t- oretical and practical contributions of experts in the
analysis of infectious disease epidemics in a single volume. Recent
collections have focused in the analyses and simulation of
deterministic and stochastic models whose aim is to identify and
rank epidemiological and social mechanisms responsible for disease
transmission. The contributions in this volume focus on the
connections between models and disease data with emphasis on the
application of mathematical and statistical approaches that
quantify model and data uncertainty. The book is aimed at public
health experts, applied mathematicians and sci- tists in the life
and social sciences, particularly graduate or advanced
undergraduate students, who are interested not only in building and
connecting models to data but also in applying and developing
methods that quantify uncertainty in the context of infectious
diseases. Chowell and Brauer open this volume with an overview of
the classical disease transmission models of Kermack-McKendrick
including extensions that account for increased levels of
epidemiological heterogeneity. Their theoretical tour is followed
by the introduction of a simple methodology for the estimation of,
the basic reproduction number,R . The use of this methodology 0 is
illustrated, using regional data for 1918-1919 and 1968 in uenza
pandemics.
Mathematical and Statistical Estimation Approaches in Epidemiology
compiles t- oretical and practical contributions of experts in the
analysis of infectious disease epidemics in a single volume. Recent
collections have focused in the analyses and simulation of
deterministic and stochastic models whose aim is to identify and
rank epidemiological and social mechanisms responsible for disease
transmission. The contributions in this volume focus on the
connections between models and disease data with emphasis on the
application of mathematical and statistical approaches that
quantify model and data uncertainty. The book is aimed at public
health experts, applied mathematicians and sci- tists in the life
and social sciences, particularly graduate or advanced
undergraduate students, who are interested not only in building and
connecting models to data but also in applying and developing
methods that quantify uncertainty in the context of infectious
diseases. Chowell and Brauer open this volume with an overview of
the classical disease transmission models of Kermack-McKendrick
including extensions that account for increased levels of
epidemiological heterogeneity. Their theoretical tour is followed
by the introduction of a simple methodology for the estimation of,
the basic reproduction number,R . The use of this methodology 0 is
illustrated, using regional data for 1918-1919 and 1968 in uenza
pandemics.
This book is an introduction to the principles and practice of
mathematical modeling in the biological sciences, concentrating on
applications in population biology, epidemiology, and resource
management. The core of the book covers models in these areas and
the mathematics useful in analyzing them, including case studies
representing real-life situations. The emphasis throughout is on
describing the mathematical results and showing students how to
apply them to biological problems while highlighting some modeling
strategies. A large number and variety of examples, exercises, and
projects are included. Additional ideas and information may be
found on a web site associated with the book. Senior undergraduates
and graduate students as well as scientists in the biological and
mathematical sciences will find this book useful. Carlos
Castillo-Chavez is professor of biomathematics in the departments
of biometrics, statistics, and theoretical and applied mechanics at
Cornell University and a member of the graduate fields of applied
mathematics, ecology and evolutionary biology, and epidemiology. H
is the recepient of numerous awards including two White House
Awards (1992 and 1997) and QEM Giant in Space Mentoring Award
(2000). Fred Brauer is a Professor Emeritus of Mathematics at the
University id Wisconsin, where he taught from 1960 to 1999, and has
also been an Honorary Professor of Mathematics at the University of
British Columbia since 1997.
Research on social networks has exploded over the last decade. To a
large extent, this has been fueled by the spectacular growth of
social media and online social networking sites, which continue
growing at a very fast pace, as well as by the increasing
availability of very large social network datasets for purposes of
research. A rich body of this research has been devoted to the
analysis of the propagation of information, influence, innovations,
infections, practices and customs through networks. Can we build
models to explain the way these propagations occur? How can we
validate our models against any available real datasets consisting
of a social network and propagation traces that occurred in the
past? These are just some questions studied by researchers in this
area. Information propagation models find applications in viral
marketing, outbreak detection, finding key blog posts to read in
order to catch important stories, finding leaders or trendsetters,
information feed ranking, etc. A number of algorithmic problems
arising in these applications have been abstracted and studied
extensively by researchers under the garb of influence
maximization. This book starts with a detailed description of
well-established diffusion models, including the independent
cascade model and the linear threshold model, that have been
successful at explaining propagation phenomena. We describe their
properties as well as numerous extensions to them, introducing
aspects such as competition, budget, and time-criticality, among
many others. We delve deep into the key problem of influence
maximization, which selects key individuals to activate in order to
influence a large fraction of a network. Influence maximization in
classic diffusion models including both the independent cascade and
the linear threshold models is computationally intractable, more
precisely #P-hard, and we describe several approximation algorithms
and scalable heuristics that have been proposed in the literature.
Finally, we also deal with key issues that need to be tackled in
order to turn this research into practice, such as learning the
strength with which individuals in a network influence each other,
as well as the practical aspects of this research including the
availability of datasets and software tools for facilitating
research. We conclude with a discussion of various research
problems that remain open, both from a technical perspective and
from the viewpoint of transferring the results of research into
industry strength applications.
This book on Infectious Disease Informatics (IDI) and
biosurveillance is intended to provide an integrated view of the
current state of the art, identify technical and policy challenges
and opportunities, and promote cross-disciplinary research that
takes advantage of novel methodology and what we have learned from
innovative applications. This book also fills a systemic gap in the
literature by emphasizing informatics driven perspectives (e.g.,
information system design, data standards, computational aspects of
biosurveillance algorithms, and system evaluation). Finally, this
book attempts to reach policy makers and practitioners through the
clear and effective communication of recent research findings in
the context of case studies in IDI and biosurveillance, providing
"hands-on" in-depth opportunities to practitioners to increase
their understanding of value, applicability, and limitations of
technical solutions. This book collects the state of the art
research and modern perspectives of distinguished individuals and
research groups on cutting-edge IDI technical and policy research
and its application in biosurveillance. The contributed chapters
are grouped into three units. Unit I provides an overview of recent
biosurveillance research while highlighting the relevant legal and
policy structures in the context of IDI and biosurveillance ongoing
activities. It also identifies IDI data sources while addressing
information collection, sharing, and dissemination issues as well
as ethical considerations. Unit II contains survey chapters on the
types of surveillance methods used to analyze IDI data in the
context of public health and bioterrorism. Specific computational
techniques covered include: text mining, time series analysis,
multiple data streams methods, ensembles of surveillance methods,
spatial analysis and visualization, social network analysis, and
agent-based simulation. Unit III examines IT and decision support
for public health event response and bio-defense. Practical lessons
learned in developing public health and biosurveillance systems,
technology adoption, and syndromic surveillance for large events
are discussed. The goal of this book is to provide an
understandable interdisciplinary IDI and biosurveillance reference
either used as a standalone textbook or reference for students,
researchers, and practitioners in public health, veterinary
medicine, biostatistics, information systems, computer science, and
public administration and policy.
This IMA Volume in Mathematics and its Applications MATHEMATICAL
APPROACHES FOR EMERGING AND REEMERGING INFECTIOUS DISEASES: MODELS,
AND THEORY METHODS is based on the proceedings of a successful one
week workshop. The pro ceedings of the two-day tutorial which
preceded the workshop "Introduction to Epidemiology and Immunology"
appears as IMA Volume 125: Math ematical Approaches for Emerging
and Reemerging Infectious Diseases: An Introduction. The tutorial
and the workshop are integral parts of the September 1998 to June
1999 IMA program on "MATHEMATICS IN BI OLOGY. " I would like to
thank Carlos Castillo-Chavez (Director of the Math ematical and
Theoretical Biology Institute and a member of the Depart ments of
Biometrics, Statistics and Theoretical and Applied Mechanics,
Cornell University), Sally M. Blower (Biomathematics, UCLA School
of Medicine), Pauline van den Driessche (Mathematics and
Statistics, Uni versity of Victoria), and Denise Kirschner
(Microbiology and Immunology, University of Michigan Medical
School) for their superb roles as organizers of the meetings and
editors of the proceedings. Carlos Castillo-Chavez, es pecially,
made a major contribution by spearheading the editing process. I am
also grateful to Kenneth L. Cooke (Mathematics, Pomona College),
for being one of the workshop organizers and to Abdul-Aziz Yakubu
(Mathe matics, Howard University) for serving as co-editor of the
proceedings. I thank Simon A. Levin (Ecology and Evolutionary
Biology, Princeton Uni versity) for providing an introduction.
This book grew out of the discussions and presentations that began
during the Workshop on Emerging and Reemerging Diseases (May 17-21,
1999) sponsored by the Institute for Mathematics and its
Application (IMA) at the University of Minnesota with the support
of NIH and NSF. The workshop started with a two-day tutorial
session directed at ecologists, epidemiologists, immunologists,
mathematicians, and scientists interested in the study of disease
dynamics. The core of this first volume, Volume 125, covers
tutorial and research contributions on the use of dynamical systems
(deterministic discrete, delay, PDEs, and ODEs models) and
stochastic models in disease dynamics. The volume includes the
study of cancer, HIV, pertussis, and tuberculosis.
Beginning graduate students in applied mathematics, scientists in
the natural, social, or health sciences or mathematicians who want
to enter the fields of mathematical and theoretical epidemiology
will find this book useful.
The goal of this book is to search for a balance between simple and
analyzable models and unsolvable models which are capable of
addressing important questions on population biology. Part I
focusses on single species simple models including those which have
been used to predict the growth of human and animal population in
the past. Single population models are, in some sense, the building
blocks of more realistic models -- the subject of Part II. Their
role is fundamental to the study of ecological and demographic
processes including the role of population structure and spatial
heterogeneity -- the subject of Part III. This book, which will
include both examples and exercises, is of use to practitioners,
graduate students, and scientists working in the field.
The 18 research articles of this volume discuss the major themes
that have emerged from mathematical and statistical research in the
epidemiology of HIV. The opening paper reviews important recent
contributions. Five sections follow: Statistical Methodology and
Forecasting, Infectivity and the HIV, Heterogeneity and HIV
Transmission Dynamics, Social Dynamics and AIDS, and The Immune
System and The HIV. In each, leading experts in AIDS epidemiology
present the recent results. Some address the role of variable
infectivity, heterogeneous mixing, and long periods of
infectiousness in the dynamics of HIV; others concentrate on
parameter estimation and short-term forecasting. The last section
looks at the interaction between the HIV and the immune system.
Increasingly, mathematical methods are being used to advantage in
addressing the problems facing humanity in managing its
environment. Problems in resource management and epidemiology
especially have demonstrated the utility of quantitative modeling.
To explore these approaches, the Center of Applied Mathematics at
Cornell University organized a conference in Fall, 1987, with the
objective of surveying and assessing the state of the art. This
volume records the proceedings of that conference. Underlying
virtually all of these studies are models of population growth,
from individual cells to large vertebrates. Cell population growth
presents the simplest of systems for study, and is of fundamental
importance in its own right for a variety of medical and
environmental applications. In Part I of this volume, Michael
Shuler describes computer models of individual cells and cell
populations, and Frank Hoppensteadt discusses the synchronization
of bacterial culture growth. Together, these provide a valuable
introduction to mathematical cell biology.
Social media is an invaluable source of time-critical information
during a crisis. However, emergency response and humanitarian
relief organizations that would like to use this information
struggle with an avalanche of social media messages that exceeds
the human capacity to process. Emergency managers, decision makers,
and affected communities can make sense of social media through a
combination of machine computation and human compassion - expressed
by thousands of digital volunteers who publish, process, and
summarize potentially life-saving information. This book brings
together computational methods from many disciplines: natural
language processing, semantic technologies, data mining, machine
learning, network analysis, human-computer interaction, and
information visualization, focusing on methods that are commonly
used for processing social media messages under time-critical
constraints, and offering more than 500 references to in-depth
information.
Social media is an invaluable source of time-critical information
during a crisis. However, emergency response and humanitarian
relief organizations that would like to use this information
struggle with an avalanche of social media messages that exceeds
the human capacity to process. Emergency managers, decision makers,
and affected communities can make sense of social media through a
combination of machine computation and human compassion - expressed
by thousands of digital volunteers who publish, process, and
summarize potentially life-saving information. This book brings
together computational methods from many disciplines: natural
language processing, semantic technologies, data mining, machine
learning, network analysis, human-computer interaction, and
information visualization, focusing on methods that are commonly
used for processing social media messages under time-critical
constraints, and offering more than 500 references to in-depth
information.
Resumen en Espanol: Existen en Espana restos o referencias de 73
presas de probable adscripcion a epoca romana cuya fecha de
construccion se situa entre los siglos I y IV, de las que 45 han
podido ser localizadas y caracterizadas con suficiente detalle. Ha
sido posible observar como los romanos desarrollaron su tecnica de
construccion de presas en la provincia hispana a traves de la
evolucion en sus tipologias constructivas y en los emplazamientos
escogidos, lo que supone el aprendizaje tras los fracasos en sus
primeras realizaciones. Abstract in English: In Spain there are the
remains of and references to 73 dams from the Roman era,
constructed between the 1st. and 4th. centuries a.C. Fourty five of
them have been located and detailed in this study.
This is a reproduction of a book published before 1923. This book
may have occasional imperfections such as missing or blurred pages,
poor pictures, errant marks, etc. that were either part of the
original artifact, or were introduced by the scanning process. We
believe this work is culturally important, and despite the
imperfections, have elected to bring it back into print as part of
our continuing commitment to the preservation of printed works
worldwide. We appreciate your understanding of the imperfections in
the preservation process, and hope you enjoy this valuable book.
++++ The below data was compiled from various identification fields
in the bibliographic record of this title. This data is provided as
an additional tool in helping to ensure edition identification:
++++ Spanish Life: A Cultural Reader For The First Year Philip
Schuyler Allen, Carlos Castillo H. Holt and Company, 1920 Spanish
language
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