STATISTICAL METHODS FOR DYNAMIC DISEASE SCREENING AND SPATIO TEMPORAL DISEASE SURVEILLANCE (HB 2024)

Disease screening and disease surveillance (DSDS) constitute two critical areas in public health, each presenting distinctive challenges primarily due to their sequential decision-making nature and complex data structures. Statistical Methods for Dynamic Disease Screening and Spatio-Temporal Disease Surveillance explores numerous recent analytic methodologies that enhance traditional techniques. The author, a prominent researcher specializing in innovative sequential decision-making techniques, demonstrates how these novel methods effectively address the challenges of DSDS.

After a concise introduction that lays the groundwork for comprehending the challenges inherent in DSDS, the book delves into fundamental statistical concepts and methods relevant to DSDS. This includes exploration of statistical process control (SPC) charts specifically crafted for sequential decision-making purposes. The subsequent chapters systematically outline recent advancements in dynamic screening system (DySS) methods, fine-tuned for effective disease screening. Additionally, the text covers both traditional and contemporary analytic methods for disease surveillance. It further introduces two recently developed R packages designed for implementing DySS methods and spatio-temporal disease surveillance techniques pioneered by the author's research team.

Features

• Presents Recent Analytic Methods for DSDS: The book introduces analytic methods for DSDS based on SPC charts. These methods effectively utilize all historical data, accommodating the complex data structure inherent in sequential decision-making processes.

• Introduces Recent R Packages: Two recent R packages, DySS and SpTe2M, are introduced. The book not only presents these packages but also demonstrates key DSDS methods using them.

• Examines Recent Research Results: The text delves into the latest research findings across various domains, including dynamic disease screening, nonparametric spatio-temporal data modeling and monitoring, and spatio-temporal disease surveillance.

• Accessible Description of Methods: Major methods are described in a manner accessible to individuals without advanced knowledge in mathematics and statistics. The goal is to facilitate a clear understanding of ideas and easy implementation.

• Real-Data Examples: To aid comprehension, the book provides several real-data examples illustrating key concepts and methods.

• Hands-on Exercises: Each chapter includes exercises to encourage hands-on practice, allowing readers to engage directly with the presented methods.

Peihua Qiu is Dean’s Professor and Founding Chair of the Department of Biostatistics at the University of Florida. He received his PhD in statistics from the Department of Statistics at the University of Wisconsin at Madison in 1996. He then worked as a senior research consulting statistician for the Biostatistics Center at the Ohio State University during 1996-1998, and as an Assistant Professor (1998-2002), Associate Professor (2002-2007), and Full Professor (2007-2013) of the School of Statistics at the University of Minnesota during 1998-2013. He was recruited to the University of Florida to develop its new Department of Biostatistics in 2013. Qiu has made substantial contributions in the research areas of jump regression analysis, image processing, statistical process control, survival analysis, dynamic disease screening, and spatio-temporal disease surveillance. So far, he has published two research monographs and over 160 research papers in refereed journals in these areas. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American Statistical Association (ASA), an elected fellow of the American Society for Quality (ASQ), an elected fellow of the Institute of Mathematical Statistics (IMS), and an elected member of the International Statistical Institute (ISI). He served as associate editor for several top statistical journals, including Journal of the American Statistical Association, Biometrics, and Technometrics. He was the Editor-in-Chief of the flagship statistical journal Technometrics during 2014-2016.