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.

Table of Contents:
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 Approaches 27  2.1.3 Chapter Overview 31  2.2 HIV Resource Allocation: Theoretical Analyses 31  2.2.1 Defining the Resource Allocation Problem 31  2.2.2 Production Functions for Prevention and Treatment Programs 35  2.2.3 Allocating Resources among Prevention and Treatment Programs 37  2.3 HIV Resource Allocation: Portfolio Analyses 39  2.3.1 Portfolio Analysis 39  2.3.2 Opiate Substitution Therapy and ART in Ukraine 40  2.3.3 Pre‐exposure Prophylaxis and ART 42  2.4 HIV Resource Allocation: A Tool for Decision Makers 44  2.4.1 REACH Model Overview 44  2.4.2 Example Analysis: Brazil 45  2.4.3 Example Analysis: Thailand 48  2.5 Discussion and Further Research 50  Acknowledgment 53  References 53  3 Adaptive Decision‐Making During Epidemics 59 Reza Yaesoubi and Ted Cohen  3.1 Introduction 59  3.2 Problem Formulation 61  3.3 Methods 63  3.3.1 The 1918 Influenza Pandemic in San Francisco, CA 63  3.3.2 Stochastic Transmission Dynamic Models 64  3.3.3 Calibration 66  3.3.4 Optimizing Dynamic Health Policies 69  3.4 Numerical Results 73  3.5 Conclusion 75  Acknowledgments 76  References 76  4 Assessing Register‐Based Chlamydia Infection Screening Strategies: A Cost‐Effectiveness Analysis on Screening Start/End Age and Frequency 81 Yu Teng, Nan Kong, and Wanzhu Tu  4.1 Introduction 81  4.2 Background Literature Review 83  4.2.1 Clinical Background on CT Infection and Control 83  4.2.2 CT Screening Programs 85  4.2.3 Computational Modeling on CT Transmission and Control 85  4.3 Mathematical Modeling 89  4.3.1 An Age‐Structured Compartmental Model 89  4.3.2 Model Parameterization and Validation 93  4.4 Strategy Assessment 98  4.4.1 Base‐Case Assessment 98  4.4.2 Sensitivity Analysis 100  4.5 Conclusions and Future Research 101  References 102  5 Optimal Selection of Assays for Detecting Infectious Agents in Donated Blood 109 Ebru K. Bish, Hadi El‐Amine, Douglas R. Bish, Susan L. Stramer, and Anthony D. Slonim  5.1 Introduction and Challenges 109  5.1.1 Introduction 109  5.1.2 The Challenges 111  5.2 The Notation and Decision Problem 113  5.2.1 Notation 114  5.2.2 Measures of Interest 115  5.2.3 Model Formulation 117  5.2.4 Relationship of the Proposed Mathematical Models to Cost‐Effectiveness Analysis 118  5.3 The Case Study of the Sub‐Saharan Africa Region and the United States 119  5.3.1 Uncertainty in Prevalence Rates 122  5.4 Contributions and Future Research Directions 123  Acknowledgments 123  References 124  6 Modeling Chronic Hepatitis C During Rapid Therapeutic Advance: Cost‐Effective Screening, Monitoring, and Treatment Strategies 129 Shan Liu  6.1 Introduction 129  6.2 Method 131  6.2.1 Modeling Disease Natural History and Intervention 132  6.2.2 Estimating Parameters for Disease Progression and Death 134  6.3 Four Research Areas in Designing Effective HCV Interventions 139  6.3.1 Cost‐Effective Screening and Treatment Strategies 139  6.3.2 Cost‐Effective Monitoring Guidelines 141  6.3.3 Optimal Treatment Adoption Decisions 141  6.3.4 Optimal Treatment Delivery in Integrated Healthcare Systems 145  6.4 Concluding Remarks 148  References 148  Part 2 Noncommunicable Disease Prevention 153  7 Modeling Disease Progression and Risk‐Differentiated Screening for Cervical Cancer Prevention 155 Adriana Ley‐Chavez and Julia L. Higle  7.1 Introduction 155  7.2 Literature Review 157  7.3 Modeling Cervical Cancer Screening 159  7.3.1 Model Components 160  7.3.2 Parameter Selection 166  7.3.3 Implementation 169  7.4 Model‐Based Analyses 171  7.4.1 Cost‐Effectiveness Analysis 171  7.4.2 Sensitivity Analysis 172  7.5 Concluding Remarks 174  References 175  8 Using Finite‐Horizon Markov Decision Processes for Optimizing Post‐Mammography Diagnostic Decisions 183 Sait Tunc, Oguzhan Alagoz, Jagpreet Chhatwal, and Elizabeth S. Burnside  8.1 Introduction 183  8.2 Model Formulations 185  8.3 Structural Properties 188  8.4 Numerical Results 193  8.5 Summary 196  Acknowledgments 196  References 197  9 Partially Observable Markov Decision Processes for Prostate Cancer Screening, Surveillance, and Treatment: A Budgeted Sampling Approximation Method 201 Jingyu Zhang and Brian T. Denton  9.1 Introduction 201  9.2 Review of POMDP Models and Benchmark Algorithms 204  9.3 A POMDP Model for Prostate Cancer Screening, Surveillance, and Treatment 206  9.4 Budgeted Sampling Approximation 209  9.4.1 Lower and Upper Bounds 209  9.4.2 Summary of the Algorithm 211  9.5 Computational Experiments 213  9.5.1 Finite‐Horizon Test Instances 213  9.5.2 Computational Experiments 214  9.6 Conclusions 217  References 219  10 Cost‐Effectiveness Analysis of Breast Cancer Mammography Screening Policies Considering Uncertainty in Women’s Adherence 223 Mahboubeh Madadi and Shengfan Zhang  10.1 Introduction 223  10.2 Model Formulation 225  10.3 Numerical Studies 231  10.4 Results 233  10.4.1 Perfect Adherence Case 233  10.4.2 General Population Adherence Case 234  10.5 Summary 236  References 237  11 An Agent‐Based Model for Ideal Cardiovascular Health 241 Yan Li, Nan Kong, Mark A. Lawley, and José A. Pagán  11.1 Introduction 241  11.2 Methodology 243  11.2.1 Agent‐Based Modeling 243  11.2.2 Model Structure 244  11.2.3 Parameter Estimation 246  11.2.4 User Interface 248  11.2.5 Model Validation 249  11.3 Results 250  11.3.1 Simulating American Adults 250  11.4 Simulating the Medicare‐Age Population and the Disease‐Specific Subpopulations 252  11.5 Future Research 254  11.6 Summary 255  References 255  Part 3 Treatment Technology and System 259  12 Biological Planning Optimization for High‐Dose‐Rate Brachytherapy and its Application to Cervical Cancer Treatment 261 Eva K. Lee, Fan Yuan, Alistair Templeton, Rui Yao, Krystyna Kiel, and James C.H. Chu  12.1 Introduction 261  12.2 Challenges and Objectives 263  12.3 Materials and Methods 265  12.3.1 High‐Dose‐Rate Brachytherapy 265  12.3.2 PET Image 266  12.3.3 Novel OR‐Based Treatment‐Planning Model 266  12.3.4 Computational Challenges and Solution Strategies 271  12.4 Validation and Results 273  12.5 Findings, Implementation, and Challenges 276  12.6 Impact and Significance 279  12.6.1 Quality of Care and Quality of Life for Patients 279  12.6.2 Advancing the Cancer Treatment Frontier 279  12.6.3 Advances in Operations Research Methodologies 280  Acknowledgment 281  References 281  13 Fluence Map Optimization in Intensity‐Modulated Radiation Therapy Treatment Planning 285 Dionne M. Aleman  13.1 Introduction 285  13.2 Treatment Plan Evaluation 288  13.2.1 Physical Dose Measures 289  13.2.2 Biological Dose Measures 291  13.3 FMO Optimization Models 292  13.3.1 Objective Functions 293  13.3.2 Constraints 295  13.3.3 Robust Formulation 297  13.4 Optimization Approaches 299  13.5 Conclusions 300  References 301  14 Sliding Window IMRT and VMAT Optimization 307 David Craft and Tarek Halabi  14.1 Introduction 307  14.2 Two‐Step IMRT Planning 309  14.3 One‐Step IMRT Planning 310  14.3.1 One‐Step Sliding Window Optimization 310  14.4 Volumetric Modulated ARC Therapy 313  14.5 Future Work for Radiotherapy Optimization 315  14.5.1 Custom Solver for Radiotherapy 315  14.5.2 Incorporating Additional Hardware Considerations into Sliding Window VMAT Planning 315  14.5.3 Trade‐Off between Delivery Time and Plan Quality 316  14.5.4 What Do We Optimize? 316  14.6 Concluding Thoughts 317  References 318  15 Modeling the Cardiovascular Disease Prevention–Treatment Trade‐Off 323 George Miller  15.1 Introduction 323  15.2 Methods 325  15.2.1 Model Overview 325  15.2.2 Model Structure 327  15.2.3 Model Inputs 331  15.3 Results 334  15.3.1 Base Case 334  15.3.2 Interaction between Prevention and Treatment Spending 335  15.3.3 Impact of Discount Rate on Cost‐Effectiveness 336  15.3.4 Optimal Spending mix 337  15.3.5 Impact of Prevention Lag on Optimal mix 338  15.3.6 Impact of Discount Rate on Optimal mix 340  15.3.7 Impact of Time Horizon on Optimal mix 340  15.3.8 Impacts of Research 341  15.4 Discussion 344  Acknowledgment 346  References 346  16 Treatment Optimization for Patients with Type 2 Diabetes 349 Jennifer Mason Lobo  16.1 Introduction 349  16.2 Literature Review 350  16.3 Model Formulation 353  16.3.1 Decision Epochs 354  16.3.2 States 354  16.3.3 Actions 355  16.3.4 Probabilities 355  16.3.5 Rewards 356  16.3.6 Value Function 356  16.4 Numerical Results 357  16.4.1 Model Inputs 357  16.4.2 Optimal Treatment Policies to Reduce Polypharmacy 358  16.5 Conclusions 362  References 363  17 Machine Learning for Early Detection and Treatment Outcome Prediction 367 Eva K. Lee  17.1 Introduction 367  17.2 Background 369  17.3 Machine Learning with Discrete Support Vector Machine Predictive Models 372  17.3.1 Modeling of Reserved‐Judgment Region for General Groups 373  17.3.2 Discriminant Analysis via Mixed‐Integer Programming 374  17.3.3 Model Variations 376  17.3.4 Theoretical Properties and Computational Strategies 379  17.4 Applying Damip to Real‐World Applications 380  17.4.1 Validation of Model and Computational Effort 381  17.4.2 Applications to Biological and Medical Problems 381  17.4.3 Applying DAMIP to UCI Repository of Machine Learning Databases 389  17.5 Summary and Conclusion 393  Acknowledgment 394  References 394  Index 401