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This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.

Die Beiträge der Wissenschaft in der Corona-Krise waren vielfältig. Sie reichten von der Impfstoffentwicklung über Aerosolforschung bis hin zu Modellierungen. Es gab viele Erfolgsmeldungen. Virologen wurden zu Medienstars. Wissenschaft, Politik und Medien sprachen zeitweise mit einer Stimme – ein Siegeszug der Wissenschaftskommunikation? Es gab aber auch überzogene Versprechungen, dünnhäutige Reaktionen bei Kritik und Einseitigkeit in der Politikberatung – ein kommunikatives Desaster für Wissenschaft, Politik und Medien?Dieses Sachbuch möchte die Diskussion zur Corona-Kommunikation in Wissenschaft, Politik und Medien anregen. Es basiert auf Fallstudien, die aus verschiedenen Perspektiven beleuchtet werden, ordnet Beispiele entlang der Kriterien etablierter Wissenschaftsorganisationen ein und formuliert Fragen an die künftige Wissenschaft und ihre Kommunikation.Ist dies eine Studie, Materialsammlung oder Streitschrift? Alles. Aber hauptsächlich ein Plädoyer: Gegen unantastbare Wahrheiten, Meinungshomogenisierung und Ausgrenzung. Für Transparenz, Pluralität und Dialog in Wissenschaft und Kommunikation.

This textbook provides basic quantitative models allowing researchers and decision makers to a) assess viability of threatened populations and evaluate the success of species reintroductions, b) estimate invasion abilities of alien species, c) evaluate the persistence of metapopulations subjected to habitat destruction and fragmentation, d) analyze policies and strategies for the sustainable harvesting of biological resources, and e) assess the course of human and nonhuman diseases and the possible containment measures. Air and water pollution, overexploitation of renewable resources (e.g. marine fish stocks and forests), massive land-use change together with climate change impact the Earth biodiversity and impair the functioning of ecosystems. Globalization increases the risk of diffusion of alien species and new pathogens.A panoply of numerical problems mainly based on real data from the ecological literature enables the reader to practice the presented modelling tools.presented modelling tools.

This straightforward primer in basic statistics and epidemiology emphasises their practical use in healthcare and public health, providing understanding of essential topics such as study design, data analysis and statistical methods used in the execution of medical research. Assuming no prior knowledge, the clarity of the text and care of presentation ensure those new to, or challenged by, these topics are given a thorough introduction without being overwhelmed by unnecessary detail. Key features: Provides an excellent grounding in the basics of both statistics and epidemiology Full step-by-step guidance on performing statistical calculations Numerous examples and exercises with detailed answers to help readers navigate these complex subjects with ease and confidence Enables students and practitioners to make sense of the many research studies that underpin evidence-based practice Fully revised and updated for this fifth edition, now with additional exercises and question and answers online for self-testing An understanding and appreciation of statistics is central to ensuring that professional practice is based on the best available evidence, in order to best treat and help the wider community. Reading this book will help students, researchers, doctors, nurses, and health managers to understand and apply the tools of statistics and epidemiology to their own practice.

Students and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students'anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engagingexplanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/

This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19.

Focussing on stochastic models for the spread of infectious diseases in a human population, this book is the outcome of a two-week ICPAM/CIMPA school on'Stochastic models of epidemics'which took place in Ziguinchor, Senegal, December 5–16, 2015. The text is divided into four parts, each based on one of the courses given at the school: homogeneous models (Tom Britton and Etienne Pardoux), two-level mixing models (David Sirl and Frank Ball), epidemics on graphs (Viet Chi Tran), and statistics for epidemic models (Catherine Larédo). The CIMPA school was aimed at PhD students and Post Docs in the mathematical sciences. Parts (or all) of this book can be used as the basis for traditional or individual reading courses on the topic. For this reason, examples and exercises (some with solutions) are provided throughout.

This book discusses globalization and its impact on human health. The population of the world grew from 1 billion in 1800 to 7 billion in 2012, and over the past 50 years the mean temperature has risen faster than ever before. Both factors continue to rise, as well as health inequalities. Our environment is changing rapidly, with tremendous consequences for our health. These changes produce complex and constantly varying interactions between the biosphere, economy, climate and human health, forcing us to approach future global health trends from a new perspective. Preventive actions to improve health, especially in low-income countries, are essential if our future is going to be a sustainable one. After a period of undeniable improvement in the health of the world's population, this improvement is likely to slow down and we will experience– at least locally – crises of the same magnitude as have been observed in financial markets since 2009. There is instability in health systems, which will worsen if preventive and buffering mechanisms do not take on a central role. We cannot exclude the possibility that the allied forces of poverty, social inequalities, climate change, industrial food and lack of governance will lead to a deterioration in the health of large sectors of the population. In low-income countries, while many of the traditional causes of death (infectious diseases) are still highly prevalent, other threats typical of affluent societies (obesity, diabetes, cardiovascular diseases) are increasing. Africa is not only affected by malaria, TB and HIV, but also by skyrocketing rates of cancer. The book argues that the current situation requires effective and coordinated multinational interventions guided by the principle of health as a common good. An entirely competition-driven economy cannot – by its very nature – address global challenges that require full international cooperation. A communal global leadership is called for.Paolo Vineis is Chair of Environmental Epidemiology at Imperial College. His current research activities focus on examining biomarkers of disease risk as well as studying the effects of climate change on non-communicable diseases.“From morality to molecules, environment to equity, climate change to cancer, and politics to pathology, this is a wonderful tour of global health – consistently presented in a clear, readable format. Really, an important contribution.”Professor Sir Michael Marmot Director, Institute of Health Equity University College London Author of “The Health Gap”“This book is a salutary and soundly argued reminder that the ‘common good'is not simply what remains after individuals and groups have appropriated the majority of societal resources: it is in fact the foundation on which any society rests and without which it collapses.” Rodolfo Saracci, International Agency for Research on Cancer, Lyon, France

This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems.Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods.This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data.Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. These analyses can provide imp

Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems.New to the Third Edition New chapter on risk scores and clinical decision rules New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines Many more exercises and examples using both Stata and SAS More than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding. Web ResourceA wealth of supporting material can be downloaded from the book's CRC Press web page, including: Real-life data sets used in the text SAS and Stata programs used for examples in the text SAS and Stata programs for special techniques covered Sample size spreadsheet

Mathematical epidemiology of infectious diseases usually involves describing the flow of individuals between mutually exclusive infection states. One of the key parameters describing the transition from the susceptible to the infected class is the hazard of infection, often referred to as the force of infection. The force of infection reflects the degree of contact with potential for transmission between infected and susceptible individuals. The mathematical relation between the force of infection and effective contact patterns is generally assumed to be subjected to the mass action principle, which yields the necessary information to estimate the basic reproduction number, another key parameter in infectious disease epidemiology. It is within this context that the Center for Statistics (CenStat, I-Biostat, Hasselt University) and the Centre for the Evaluation of Vaccination and the Centre for Health Economic Research and Modelling Infectious Diseases (CEV, CHERMID, Vaccine and Infectious Disease Institute, University of Antwerp) have collaborated over the past 15 years. This book demonstrates the past and current research activities of these institutes and can be considered to be a milestone in this collaboration. This book is focused on the application of modern statistical methods and models to estimate infectious disease parameters. We want to provide the readers with software guidance, such as R packages, and with data, as far as they can be made publicly available.

The Institute for Mathematical Sciences at the National University of Singapore hosted a research program on Mathematical Modeling of Infectious Diseases: Dynamics and Control from 15 August to 9 October 2005. As part of the program, tutorials for graduate students and junior researchers were given by leading experts in the field.This invaluable volume is a collection of three expanded lecture notes of those tutorials which cover a wide range of topics including basic mathematical details for various epidemic models, statistical distribution theory, and applications with real-life examples with implications for health policy makers.

Based on lecture notes of two summer schools with a mixed audience from mathematical sciences, epidemiology and public health, this volume offers a comprehensive introduction to basic ideas and techniques in modeling infectious diseases, for the comparison of strategies to plan for an anticipated epidemic or pandemic, and to deal with a disease outbreak in real time. It covers detailed case studies for diseases including pandemic influenza, West Nile virus, and childhood diseases. Models for other diseases including Severe Acute Respiratory Syndrome, fox rabies, and sexually transmitted infections are included as applications. Its chapters are coherent and complementary independent units. In order to accustom students to look at the current literature and to experience different perspectives, no attempt has been made to achieve united writing style or unified notation. Notes on some mathematical background (calculus, matrix algebra, differential equations, and probability) have been prepared and may be downloaded at the web site of the Centre for Disease Modeling (www.cdm.yorku.ca).

Lo scopo di questo libro consiste nel presentare ai radiologi problemi e metodi statistici in modo agile, ponendo l'accento su argomenti statistici frequentemente impiegati in studi radiologici (es. test non-parametrici, paragone di sensibilità e specificità nelle diverse modalità di imaging, differenze fra applicazioni cliniche e di screening di test radiologici, ecc). I test saranno presentati partendo da un problema radiologico e tutti gli esempi statistici di metodi e applicazioni saranno tratti dal mondo radiologico.

Studying Human Populations is a textbook for graduate students and research workers in social statistics and related subject areas. It follows a novel curriculum developed around the basic statistical activities of sampling, measurement and inference. Statistics is defined broadly as making decisions in the presence of uncertainty that arises as a consequence of limited resources available for collecting information. A connecting link of the presented methods is the perspective of missing information, catering for a diverse class of problems that include nonresponse, imperfect measurement and causal inference. In principle, any problem too complex for our limited analytical toolkit could be converted to a tractable problem if some additional information were available. Ingenuity is called for in declaring such (missing) information constructively, but the universe of problems that we can address is wide open, not limited by a discrete set of procedures. The monograph aims to prepare the reader for the career of an independent social statistician and to serve as a reference for methods, ideas for and ways of studying human populations: formulation of the inferential goals, design of studies, search for the sources of relevant information, analysis and presentation of results. Elementary linear algebra and calculus are prerequisites, although the exposition is quite forgiving, especially in the first few chapters. Familiarity with statistical software at the outset is an advantage, but it can be developed concurrently with studying the text.

Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained volume, an outgrowth of an'International Conference on Statistics in Health Sciences,'covers a wide range of topics pertaining to new statistical methods in the health sciences. The chapters, written by leading experts in their respective fields, are thematically divided into the following areas: prognostic studies and general epidemiology, pharmacovigilance, quality of life, survival analysis, clustering, safety and efficacy assessment, clinical design, models for the environment, genomic analysis, and animal health. This comprehensive volume will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.

The International Symposium on Medical Data Analysis is an important - riodical opportunity to exchange ideas and?rst-hand experiences with groups interested in the medical applications of innovative hardware and software tools. The massive information available through continuous improvements in the various modeling approaches to Medical Data Analysis is re?ected in the - sults, dealing with quite di?erent topics, presented during the Third Edition of the Symposium (ISMDA 2002). They have been grouped into the following four categories: (1) Data Mining and Decision Support Systems; (2) Medical Informatics and Modeling; (3) Time-Series Analysis; and (4) Medical Imaging. In setting up the symposium program we tried to avoid, even with the sho- age of time, parallel sessions. Thus, all participants had the chance to catch all the oral presentations, and we hope that this third proceedings volume will extend this chance also to non-participants. As for the previous volumes, it c- tains extensive up-to-date chapters on Medical Data Analysis, packed with ideas, suggestions, and solutions to many problems typical of this?eld.

This is the second edition of this text on logistic regression methods, ori- nally published in 1994. As in the first edition, each chapter contains a presentation of its topic in “lecture-book” format together with objectives, an outline, key formulae, practice exercises, and a test. The “lecture-book” has a sequence of illust- tions and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition has expanded the first edition by adding five new ch- ters and a new appendix. The five new chapters are Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data: GEE Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11–13 extend logistic regression to gen- alized estimating equations (GEE) and other methods for analyzing cor- lated response data. The appendix is titled “Computer Programs for Logistic Regression” and p- vides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The so- ware packages considered are SAS Version 8.0, SPSS Version 10.0, and STATA Version 7.0.

Offers an in-depth report on advanced statistical tools for public health disease surveillance, which is the result of a prestigious World Health Organisation (WHO) and EU Biomed programme initiative. Traditionally, the role of public health disease surveillance has been to identify and evaluate morbidity and mortality but increasingly, more sophisticated methods are being applied as the authorities extend their studies to include control and prevention of disease. This book brings together leading experts to discuss complex methodologies for the statistical evaluation of disease mapping and risk assessment. It includes a broad variety of statistical techniques and where appropriate, examples are included on topical issues such as the analysis of putative health hazards. For easy reference the text is presented in five distinct sections, each with an introductory review: • Disease Mapping • Clustering of Disesase • Ecological Analysis • Risk Assessment for Putative Sources of Hazard • Public Health Applications and Case Studies Representative of the most pertinent issues within disease surveillance and mapping, this book will provide an accessible overview for statisticians and epidemiologists.