In a series of case studies of sexually transmitted disease and HIV/AIDS from around Africa, contributors examine the social, cultural, and political-economic bases of risk, transmission, and response to epidemic disease. This book brings together major contributions to the historical study of epidemic disease in developing countries and considers how particular constellations of cultural, social, political, and economic factors in different countries have affected the historical patterns of disease and collective (official and community) response to them. This book is a companion volume to "Sex, Disease, and Society: A Comparative History of Sexually Transmitted Diseases and HIV/AIDS in Asia and the Pacific" (Greenwood, 1997).

From this endeavor to provide insight into the conjunctions and disjunctions between the histories of STDs and the AIDS pandemic in Sub-Saharan Africa certain common issues have emerged. These include medical ambiguity and epidemiologic diversity; cultural change; racism; gender, labor migration, and economic instability; and the practice of biomedicine and epidemiology in African contexts. All of these factors are embedded in the colonial legacy and post-colonial political economic conditions across the continent.

Over the past fifty years, the case-control method, and to a lesser extent its case-based variants, have become the most important tools for the investigator of health problems. The case control method is the study of persons with the disease and a suitable control group of persons who do not have the disease. The book helps readers address a number of general and specific questions dealing with the case-control and other case-based methods, including questions of how to design and implement a case-control study that minimizes biases, how to analyze the data to appropriately deal with confounding variables and help identify reactions, and how to interpret data and present the results from a case-control study.

Immunizationisoneofthegreatadvancesinpublichealth. Figure0. 1showsacamel with a solar-powered refrigerator on his back carrying vaccines across a hot desert to the far reaches of civilization. Many vaccines contain live viruses that need to be kept cold, or the vaccine viruses will die, and the vaccines will lose their ability to produce an immune response. Thus a continuous chain of refrigeration, the cold chain, from the origin to delivery of some vaccines needs to be maintained. The inspiration of the camel image is that it represents the dedication of the world to bring vaccines to everyone. The ?rst major success, and the origin of the word vaccination (vacca for cow), was Jenner's introducing cowpox-based vaccine against smallpox in the late 18th century. After nearly a century hiatus, at the end of the 19th century, inoculations against cholera, typhoid, plague (caused by bacteria) and rabies (caused by a virus) were developed. By the early 20th century, statisticians of the stature of Karl Pe- son, Major Greenwood, and Udny Yule were heartily involved in discussions of evaluating these vaccines in the ?eld. In the 1920s, new vaccines included pert- sis, diptheria, tetanus, and bacille Calmette-Guerin ' against tuberculosis. The 1930s saw development of yellow fever, in?uenza, and rickettsia vaccines. After World War II, the advent of cell cultures in which viruses could grow enabled production of polio vaccine and vaccines against measles, mumps, rubella, varicella, and a- novirus, among others (Plotkin et al 2008).

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.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

This book provides detailed and updated knowledge about medically important 'Big Four' venomous snakes of India (Indian spectacled cobra, Indian common krait, Indian Russell's viper, and Indian saw-scaled viper). This book essentially covers the snakebite problem in the world with particular reference to Asia and India. It discusses the evolution and systematics of venomous snakes, emphasizing 'Big Four' venomous snakes of India; the evolution and composition of venoms determined by traditional biochemical and modern proteomic analyses. It also describes the pharmacological properties of enzymatic and non-enzymatic toxins of 'Big Four' venomous snakes of India. Different chapters discuss exciting topics such as species-specific and geographical differences in venom composition and its impact on pathophysiology and clinical manifestations of snakebite envenomation in India, biomedical application of Indian snake venom toxins; production and quality assessment of commercial antivenom, prevention, and treatment of snakebite in India, adverse effects of antivenom including strategies to combat antivenom reactions inpatient. This book caters to toxinologists, pharmacologists, zoologists, antivenom manufacturers, biochemists, clinicians, evolutionary biologists, herpetologists, and informed non-specialists interested to know about the Indian snake venoms.

Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient's individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.

Simulating for a crisis is far more than creating a simulation of a crisis situation. In order for a simulation to be useful during a crisis, it should be created within the space of a few days to allow decision makers to use it as quickly as possible. Furthermore, during a crisis the aim is not to optimize just one factor, but to balance various, interdependent aspects of life. In the COVID-19 crisis, decisions had to be made concerning e.g. whether to close schools and restaurants, and the (economic) consequences of a 3 or 4-week lock-down had to be considered. As such, rather than one simulation focusing on a very limited aspect, a framework allowing the simulation of several different scenarios focusing on different aspects of the crisis was required. Moreover, the results of the simulations needed to be easily understandable and explainable: if a simulation indicates that closing schools has no effect, this can only be used if the decision makers can explain why this is the case. This book describes how a simulation framework was created for the COVID-19 crisis, and demonstrates how it was used to simulate a wide range of scenarios that were relevant for decision makers at the time. It also discusses the usefulness of the approach, and explains the decisions that had to be made along the way as well as the trade-offs. Lastly, the book examines the lessons learned and the directions for the further development of social simulation frameworks to make them better suited to crisis situations, and to foster a more resilient society.

This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it.

This book comprehensively reviews various vector-borne diseases and their control methods. It discusses morphology, life history, and pathogenicity of protozoan and helminth parasites. Further, it analyzes host-parasite interactions and their adaptation within the host system for understanding parasitic infections. The book discusses the complex life cycle, biochemical adaptations, and molecular biology of the parasites. It investigates the immunological response to different infectious agents and explores new targets for combined therapeutic approaches. It also summarizes the evolution of parasitism and the ecology of parasites of the different phylum. Lastly, it provides information on vector biology emphasizing the role of basic vector research in developing future disease control methods and improving upon the existing approaches.

This book addresses the COVID-19 pandemic from a quantitative perspective based on mathematical models and methods largely used in nonlinear physics. It aims to study COVID-19 epidemics in countries and SARS-CoV-2 infections in individuals from the nonlinear physics perspective and to model explicitly COVID-19 data observed in countries and virus load data observed in COVID-19 patients. The first part of this book provides a short technical introduction into amplitude spaces given by eigenvalues, eigenvectors, and amplitudes.In the second part of the book, mathematical models of epidemiology are introduced such as the SIR and SEIR models and applied to describe COVID-19 epidemics in various countries around the world. In the third part of the book, virus dynamics models are considered and applied to infections in COVID-19 patients. This book is written for researchers, modellers, and graduate students in physics and medicine, epidemiology and virology, biology, applied mathematics, and computer sciences. This book identifies the relevant mechanisms behind past COVID-19 outbreaks and in doing so can help efforts to stop future COVID-19 outbreaks and other epidemic outbreaks. Likewise, this book points out the physics underlying SARS-CoV-2 infections in patients and in doing so supports a physics perspective to address human immune reactions to SARS-CoV-2 infections and similar virus infections.

Handbook on the Toxicology of Metals, Fourth Edition bridges the gap between established knowledgebase and new advances in metal toxicology to provide one essential reference for all those involved in the field. This book provides comprehensive coverage of basic toxicological data, emphasizing toxic effects primarily in humans, but also those of animals and biological systems in vitro. The fourth edition also contains several new chapters on important topics such as nanotoxicology, metals in prosthetics and dental implants, gene-environment interaction, neurotoxicology, metals in food, renal, cardiovascular, and diabetes effects of metal exposures and more. Volume I covers "General Considerations" and Volume II is devoted to "Specific Metals." A multidisciplinary resource with contributions from internationally-recognized experts, the fourth edition of the Handbook on the Toxicology of Metals is a prominent and indispensable reference for toxicologists, physicians, pharmacologists, engineers, and all those involved in the toxicity of metals.

Fighting an Invisible Enemy narrates the founding and growth of the internationally. renowned National Institute for Communicable Diseases (NICD) in South Africa, from its foundations in the early twentieth century as the South African Institute for Medical Research to, later, the National Institute for Virology. It started humbly, as did many of its sister public health institutions around the world, and faced daunting obstacles: financial restrictions, bureaucratic straitjacketing, international isolation during the apartheid era and, in later years, the calumny of governmental AIDS denial. Following the triumph of the eradication of the once dreaded smallpox, the NICD plays a crucial role in the ongoing global effort to eradicate polio. While South Africa carries the misfortune of the largest HIV/AIDS pandemic in the world, the institute's HIV research unit has become a world leader.

More remote from public notice are the laboratories and epidemiologists supporting the constant surveillance of communicable diseases and the alerts they provide for impending outbreaks or pandemics, such as Ebola or the Covid-19 pandemic.

The NICD is a flagship organisation in public health in South Africa and this book, by its first executive director and internationally recognised virologist Dr Barry Schoub, paints a vivid portrait of its accomplishments. Enhanced by a collection of images of its projects and facilities, the bookwill be of interest to public health specialists and activists, as well as a more general audience.

This graduate-level text provides a survey of the logic and reasoning underpinning statistical analysis, as well as giving a broad-brush overview of the various statistical techniques that play a major roll in scientific and social investigations. Arranged in rough historical order, the text starts with the ideas of provability that underpin statistical methods and progresses through the developments of the nineteenth and twentieth centuries to modern concerns and solutions. Assuming only a basic level of Mathematics and with numerous examples and illustrations, this text presents a valuable resource not only to the experienced researcher but also to the student, by complementing courses in a wide range of substantive areas and enabling the reader to rise above the details in order to see the overall structure of the subject.

Now in its second edition, this book provides a state of the art overview on basic concepts of epigenetic epidemiology and a comprehensive review of the rapidly evolving field of human epigenetics. Epigenetics plays an important role in shaping who we are and contributes to our prospects of health and disease. Unlike our genetic inheritance, our epigenome is malleable throughout the lifecourse and is shaped by our environmental experiences. Population-based epidemiologic studies increasingly incorporate epigenetic components. These so called epigenome-wide association studies (EWAS) contribute substantially to our understanding of the relevance of epigenetic marks, such as DNA methylation, histone modification, and non-coding RNAs for disease causation. Written by leading experts in the field, the book opens with a comprehensive introduction of the principles of epigenetic epidemiology and discusses challenges in study design, analysis, and interpretation. It summarizes the latest advances in epigenetic laboratory techniques, the influence of age and environmental factors on shaping the epigenome, the epigenetic clock, and the role of epigenetics in the developmental origins hypothesis. The final part focuses on epigenetic epidemiology of various health conditions such as imprinting disorders, cancer, infectious diseases, inflammation and rheumatoid arthritis, asthma, metabolic disorder and vascular disease, as well as neurodevelopmental and psychiatric disorders. Given its scope, Epigenetic Epidemiology is an indispensable resource for researchers working in the field of human epigenetics.

The book presents advanced AI based technologies in dealing with COVID-19 outbreak and provides an in-depth analysis of variety of COVID-19 datasets throughout globe. It discusses recent artificial intelligence based algorithms and models for data analysis of COVID-19 symptoms and its possible remedies. It provides a unique opportunity to present the work on state-of-the-art of modern artificial intelligence tools and technologies to track and forecast COVID-19 cases. It indicates insights and viewpoints from scholars regarding risk and resilience analytics for policy making and operations of large-scale systems on this epidemic. A snapshot of the latest architectures, frameworks in machine learning and data science are also highlighted to gather and aggregate data records related to COVID-19 and to diagnose the virus. It delivers significant research outcomes and inspiring new real-world applications with respect to feasible AI based solutions in COVID-19 outbreak. In addition, it discusses strong preventive measures to control such pandemic.

"The Advances in Bioethics" series is devoted to publishing collections of original papers and multi-authored volumes that advance the field of bioethics either by exploring new areas, or by taking new approaches to traditional areas. Although the series is published in English, its scope is international, and manuscripts are welcome from authors throughout the world. Divided into three sections this volume covers: Human Rights, Public Safety, and Public Policy; Practitioner Responsibilities During Times of Epidemics; Global Dimensions of Epidemics - each chapter in the book goes in depth to discuss the issues surrounding the topic in question and combines a theoretical thought process with practical application.

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 engaging explanations 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/

'A brilliant expose' - Danny Dorling Covid-19 has exposed the limits of a neoliberal public health orthodoxy. But instead of imagining radical change, the left is stuck in a rearguard action focused on defending the NHS from the wrecking ball of privatisation. Public health expert Christopher Thomas argues that we must emerge from Covid-19 on the offensive - with a bold, new vision for our health and care. He maps out five new frontiers for public health and imagines how we can move beyond safeguarding what we have to a radical expansion of the principles put forward by Aneurin Bevan, the founder of the NHS, over 70 years ago. Beyond recalibrating our approach to healthcare services, his blueprint includes a fundamental redesign of our economy through Public Health Net Zero; a bold new universal public health service fit to address the real causes of ill health; and a major recalibration in the efforts against the epidemiological reality of an era of pandemics.

This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.