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Informationen zum Autor NAN KONG, PhD, is Associate Professor in the Weldon School of Biomedical Engineering at Purdue University. Dr. Kong is a member of INFORMS and SMDM, and his research interests include healthcare resource allocation, medical decision-making, and hospital operations management. SHENGFAN ZHANG, PhD, is Assistant Professor in the Department of Industrial Engineering at the University of Arkansas. Dr. Zhang is a member of INFORMS and IISE, and her research interests include mathematical modeling of stochastic systems, medical decision-making, and health analytics. Klappentext A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research. Inhaltsverzeichnis Contributors xiii Preface xvii Part 1 Infectious Disease Control and Management 1 1 Optimization in Infectious Disease Control and Prevention: Tuberculosis Modeling Using Microsimulation 3 Sze¿chuan Suen 1.1 Tuberculosis Epidemiology and Background 4 1.1.1 TB in India 5 1.2 Microsimulations for Disease Control 6 1.3 A Microsimulation for Tuberculosis Control in India 8 1.3.1 Population Dynamics 9 1.3.2 Dynamics of TB in India 9 1.3.3 Activation 10 1.3.4 TB Treatment 11 1.3.5 Probability Conversions 13 1.3.6 Calibration and Validation 14 1.3.7 Intervention Policies and Analysis 16 1.3.8 Time Horizons and Discounting 18 1.3.9 Incremental Cost¿Effectiveness Ratios and Net Monetary Benefits 19 1.3.10 Sensitivity Analysis 22 1.4 Conclusion 22 References 23 2 Saving Lives with Operations Research: Models to Improve HIV Resource Allocation 25 Sabina S. Alistar and Margaret L. Brandeau 2.1 Introduction 25 2.1.1 Background 25 2.1.2 Modeling Ap...
