Editors:
- Explores current and potential statistical methodology systems for global health research
- Features data using R
- Provides researchers and students access to new methods for collecting new data
Part of the book series: ICSA Book Series in Statistics (ICSABSS)
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Table of contents (17 chapters)
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Front Matter
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Data Acquisition and Management
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Front Matter
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Essential Statistical Methods
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Front Matter
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Advanced Statistical Methods
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Front Matter
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About this book
Contemporary global health and epidemiology involves a myriad of medical and health challenges, including inequality of treatment, the HIV/AIDS epidemic and its subsequent control, the flu, cancer, tobacco control, drug use, and environmental pollution. In addition to its vast scales and telescopic perspective; addressing global health concerns often involves examining resource-limited populations with large geographic, socioeconomic diversities. Therefore, advancing global health requires new epidemiological design, new data, and new methods for sampling, data processing, and statistical analysis. This book provides global health researchers with methods that will enable access to and utilization of existing data.
Featuring contributions from both epidemiological and biostatistical scholars, this book is a practical resource for researchers, practitioners, and students in solving global health problems in research, education, training, and consultation.
Keywords
- Advance Global Health Methods
- Biostatistics in Global Health
- Satellite imagery maps
- GIS/GPS technologies
- Probability sampling in Global Health
- Psychometric evaluation
- Global disease burden analysis
- Global epidemiology
- HIV/AIDS epidemiology
- Cross-country association
- Behavioral epidemiology
- Bayesian spatiotemporal modeling of HIV
Editors and Affiliations
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Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, USA
Xinguang Chen
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School of Social Work, University of North Carolina, Chapel Hill, USA
(Din) Ding-Geng Chen
About the editors
Professor (Din) Ding-Geng Chen, PhD is an elected fellow of the American Statistical Association, the Wallace Kuralt Distinguished Professor at the School of Social Work, and a professor of biostatistics at the Gillings School of Global Public Health, University of North Carolina at Chapel Hill, USA. He is also an extraordinary professor at the department of statistics, University of Pretoria, South Africa. Professor Chen was a professor in biostatistics at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University, USA. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte-Carlo simulations, clinical trial biostatistics and public health statistics. Professor Chen has more than 200 referred professional publications, and he has co-authored/co-edited 28 books on clinical trial methodology, meta-analysis and public health applications. He has been invited nationally and internationally to give speeches on his research.
Bibliographic Information
Book Title: Statistical Methods for Global Health and Epidemiology
Book Subtitle: Principles, Methods and Applications
Editors: Xinguang Chen, (Din) Ding-Geng Chen
Series Title: ICSA Book Series in Statistics
DOI: https://doi.org/10.1007/978-3-030-35260-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-35259-2Published: 14 April 2020
Softcover ISBN: 978-3-030-35262-2Published: 14 April 2021
eBook ISBN: 978-3-030-35260-8Published: 13 April 2020
Series ISSN: 2199-0980
Series E-ISSN: 2199-0999
Edition Number: 1
Number of Pages: XV, 413
Number of Illustrations: 32 b/w illustrations, 129 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Statistical Theory and Methods, Biostatistics, Epidemiology