Author Description

<p>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. <b>Statistical Methods for Dynamic Disease Screening and Spatio-Temporal Disease Surveillance</b> 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.</p><p>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.</p><p><b>Features</b></p><p><b>• Presents Recent Analytic Methods for DSDS:</b> 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.</p><p>• <b>Introduces Recent R Packages:</b> Two recent R packages, <b>DySS</b> and <b>SpTe2M</b>, are introduced. The book not only presents these packages but also demonstrates key DSDS methods using them.</p><p>• <b>Examines Recent Research Results:</b> The text delves into the latest research findings across various domains, includin