New Horizons In Modeling And Simulation For Social Epidemiology And Public Health

An introduction to state-of-the-art modeling and simulation approaches for social and economic determinants of population health New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a comprehensive introduction to modeling and simulation that addresses the many complex research questions in social epidemiology and public health. This book highlights a variety of practical applications and illustrative examples with a focus on modeling and simulation approaches for the social and economic determinants of population health. The book contains classic case examples in agent-based modeling (ABM) as well as essential information on ABM applications to public health including for infectious disease modeling, obesity, and tobacco control. This book also surveys applications of microsimulation (MSM) including of tax-benefit policies to project impacts of the social determinants of health. Specifically, this book: Provides an overview of the social determinants of health and the public health significance of addressing the social determinants of health Gives a conceptual foundation for the application of ABM and MSM to study the social determinants of health Offers methodological introductions to both ABM and MSM approaches with illustrative examples Includes cutting-edge systematic reviews of empirical applications of ABM and MSM in the social sciences, social epidemiology, and public health Discusses future directions for empirical research using ABM and MSM, including integrating aspects of both ABM and MSM and implications for public health policies Written for a broad audience of policy analysts, public planners, and researchers and practitioners in public health and public policy including social epidemiologists, New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a fundamental guide to the social determinants of health and state-of-the-art applications of ABM and MSM to studying the social and economic determinants of population health.

An introduction to state-of-the-art modeling and simulation approaches for social and economic determinants of population health New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a comprehensive introduction to modeling and simulation that addresses the many complex research questions in social epidemiology and public health. This book highlights a variety of practical applications and illustrative examples with a focus on modeling and simulation approaches for the social and economic determinants of population health. The book contains classic case examples in agent-based modeling (ABM) as well as essential information on ABM applications to public health including for infectious disease modeling, obesity, and tobacco control. This book also surveys applications of microsimulation (MSM) including of tax-benefit policies to project impacts of the social determinants of health. Specifically, this book: Provides an overview of the social determinants of health and the public health significance of addressing the social determinants of health Gives a conceptual foundation for the application of ABM and MSM to study the social determinants of health Offers methodological introductions to both ABM and MSM approaches with illustrative examples Includes cutting-edge systematic reviews of empirical applications of ABM and MSM in the social sciences, social epidemiology, and public health Discusses future directions for empirical research using ABM and MSM, including integrating aspects of both ABM and MSM and implications for public health policies Written for a broad audience of policy analysts, public planners, and researchers and practitioners in public health and public policy including social epidemiologists, New Horizons in Modeling and Simulation for Social Epidemiology and Public Health offers a fundamental guide to the social determinants of health and state-of-the-art applications of ABM and MSM to studying the social and economic determinants of population health.

With the majority of graduate nurses going on to work in primary health, Community Health and Wellness provides the reader with the knowledge and tools they need to practise competently and safely in the community. The book provides an overview of primary healthcare principles and how to apply them, enabling a deep understanding of the role of the community health nurse in providing holistic individual and community care within an interdisciplinary healthcare environment. Continuous real-world case studies throughout enable the reader to understand how the social determinants of health influence each family member's life, their communities and the challenges they face. This seventh edition has been written by nursing experts Dr Jill Clendon and Dr Ailsa Munns, and incorporates feedback from students, users and practising community health nurses to ensure it meets the needs of today's students as they embark on their career beyond academia. Takes a unique socio-ecological approach to community health - including health promotion, health literacy and a range of health interventions Key points and information boxes allow easy navigation Case studies link theory and principles to practice Templates and tools to conduct community assessments Links to resources for further reflection An eBook included in all print purchases Additional resources on Evolve: eBook on VitalSource Instructor resources: Power Points Tutorial Activities Image Library Critical Thinking Questions and Answers Student resources: Online Care Plans Online Material - Chapter 10 Research Guide: from question to solution Weblinks Significant updates throughout More content on public health and infectious disease management, including current pandemics and epidemics Improved content on cultural safety and care planning

This book presents different techniques and methodologies used to improve the intelligent decision-making process and increase the likelihood of success in companies of different sectors such as Financial Services, Education, Supply Chain, Energy Systems, Health Services, and others. The book contains and consolidates innovative and high-quality research contributions regarding the implementation of techniques and methodologies applied in different sectors. The scope is to disseminate current trends knowledge in the implementation of artificial intelligence techniques and methodologies in different fields such as: Logistics, Software Development, Big Data, Internet of Things, Simulation, among others. The book contents are useful for Ph.D. researchers, Ph.D. students, master and undergraduate students of different areas such as Industrial Engineering, Computer Science, Information Systems, Data Analytics, and others.

A thorough, practical reference on the social patterns behind health outcomes Methods in Social Epidemiology provides students and professionals with a comprehensive reference for studying the social distribution and social determinants of health. Covering the theory, models, and methods used to measure and analyze these phenomena, this book serves as both an introduction to the field and a practical manual for data collection and analysis. This new second edition has been updated to reflect the field's tremendous growth in recent years, including advancements in statistical modeling and study designs. New chapters delve into genetic methods, structural cofounding, selection bias, network methods, and more, including new discussion on qualitative data collection with disadvantaged populations. Social epidemiology studies the way society's innumerable social interactions, both past and present, yields different exposures and health outcomes between individuals within populations. This book provides a thorough, detailed overview of the field, with expert guidance toward the real-world methods that fuel the latest advances. Identify, measure, and track health patterns in the population Discover how poverty, race, and socioeconomic factors become risk factors for disease Learn qualitative data collection techniques and methods of statistical analysis Examine up-to-date models, theory, and frameworks in the social epidemiology sphere As the field continues to evolve, researchers continue to identify new disease-specific risk factors and learn more about how the social system promotes and maintains well-known exposure disparities. New technology in data science and genomics allows for more rigorous investigation and analysis, while the general thinking in the field has become more targeted and attentive to causal inference and core assumptions behind effect identification. It's an exciting time to be a part of the field, and Methods in Social Epidemiology provides a solid reference for any student, researcher, or faculty in public health.

This open access book is a practical introduction to multilevel modelling or multilevel analysis (MLA) – a statistical technique being increasingly used in public health and health services research. The authors begin with a compelling argument for the importance of researchers in these fields having an understanding of MLA to be able to judge not only the growing body of research that uses it, but also to recognise the limitations of research that did not use it. The volume also guides the analysis of real-life data sets by introducing and discussing the use of the multilevel modelling software MLwiN, the statistical package that is used with the example data sets. Importantly, the book also makes the training material accessible for download – not only the datasets analysed within the book, but also a freeware version of MLwiN to allow readers to work with these datasets. The book’s practical review of MLA comprises: Theoretical, conceptual, and methodological background Statistical background The modelling process and presentation of research Tutorials with example datasets Multilevel Modelling for Public Health and Health Services Research: Health in Context is a practical and timely resource for public health and health services researchers, statisticians interested in the relationships between contexts and behaviour, graduate students across these disciplines, and anyone interested in utilising multilevel modelling or multilevel analysis. “Leyland and Groenewegen’s wealth of teaching experience makes this book and its accompanying tutorials especially useful for a practical introduction to multilevel analysis.” ̶ Juan Merlo, Professor of Social Epidemiology, Lund University “Comprehensive and insightful. A must for anyone interested in the applications of multilevel modelling to population health”. ̶ S. (Subu) V. Subramanian, Professor of Population Health and Geography, Harvard University

Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy. Presents a comprehensive, two-part volume written by leading subject experts Provides a unique breadth and depth of content coverage Addresses the most cutting-edge developments in the field Includes chapters on Ebola and the Zika virus; topics which have grown in prominence and scholarly output

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.